{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [],
   "source": [
    "import gym\n",
    "from gym import envs\n",
    "import torch\n",
    "import numpy as np\n",
    "from tqdm import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "[ 0.02489755 -0.19822834 -0.03863851  0.3147768 ] 1.0 False {}\n"
     ]
    }
   ],
   "source": [
    "#创建环境\n",
    "env = gym.make('CartPole-v0')\n",
    "# envs.registry.all()\n",
    "state1 = env.reset()\n",
    "# 测试环境\n",
    "action = env.action_space.sample()\n",
    "print(action)\n",
    "state ,reward, done, info = env.step(action)\n",
    "print(state,reward,done,info)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = torch.nn.Sequential(\n",
    "    torch.nn.Linear(4,150),\n",
    "    torch.nn.ReLU(),\n",
    "    torch.nn.Linear(150,2),\n",
    "    torch.nn.Softmax() # 返回每个动作的概率分布\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [],
   "source": [
    "def discount_reward(rewards,gamma=0.99): # 计算贴现奖励值\n",
    "    # gamma 贴现因子\n",
    "    lenr = len(rewards)\n",
    "    discount_return = torch.pow(gamma,torch.arange(lenr).float())*rewards\n",
    "    discount_return /= discount_return.max() # 归一化到[0,1]\n",
    "    return discount_return\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [],
   "source": [
    "#损失函数\n",
    "def loss_fn(preds,r):\n",
    "    return -1*torch.sum(r*torch.log(preds)) # 损失函数设置\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [],
   "source": [
    "optimizer = torch.optim.Adam(model.parameters(),lr = 0.0009)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [],
   "source": [
    "MAX_DUR = 200\n",
    "MAX_EPISODES = 500\n",
    "gamma = 0.99\n",
    "score = []\n",
    "losses = []\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/500 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 500/500 [00:07<00:00, 69.48it/s] \n"
     ]
    }
   ],
   "source": [
    "for episoe in tqdm(range(MAX_EPISODES)):\n",
    "    current_state = env.reset()\n",
    "    done = False\n",
    "    transitions = []\n",
    "\n",
    "    for t in range(MAX_DUR):\n",
    "        act_prob = model(torch.from_numpy(current_state).float())\n",
    "        action = np.random.choice(np.array([0,1]),p = act_prob.data.numpy())\n",
    "        prev_state =current_state\n",
    "        current_state,_,done,info = env.step(action)\n",
    "        transitions.append((prev_state,action,t+1)) # 保持多久奖励值就是多少\n",
    "\n",
    "        if done:\n",
    "            break\n",
    "\n",
    "    ep_len = len(transitions)\n",
    "    score.append(ep_len)\n",
    "\n",
    "    reward_batch = torch.tensor([r for (s,a,r) in transitions],dtype=torch.float32).flip(dims=(0,))\n",
    "    disc_rewards = discount_reward(reward_batch)\n",
    "\n",
    "    state_batch = torch.tensor([s for (s,a,r) in transitions],dtype=torch.float32)\n",
    "    action_batch = torch.tensor([a for (s,a,r) in transitions],dtype=torch.float32)\n",
    "\n",
    "    pred_batch = model(state_batch) # 返回每个状态的预测，每个动作的概率分布\n",
    "    prob_batch = pred_batch.gather(dim=1,index=action_batch.long().view(-1,1)).squeeze()\n",
    "\n",
    "    loss = loss_fn(prob_batch,disc_rewards)\n",
    "    optimizer.zero_grad()\n",
    "    loss.backward()\n",
    "    optimizer.step()\n",
    "    losses.append(loss.item())\n",
    "    \n",
    "losses = np.array(losses)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fb5002c24c0>]"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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3Kg1ATyD4WSICuXg02hNJxA2l2HC35Z1RVEpIoBAEQRCEgodf24K129vx/Nqd8g1yKtSW/Swr1MZZTCxBFGmIiLKY2oIi7h/lLB4SKARBEERkyKV0fDGwB29XFk8ObaksKDKBYMEK7IbxzOIR04wV/YoCJFAIgiAIwocwx242tdjPrZKxoARMM46pXTzi8cRS9397aQPWbt/nf5AiUNDJAgmCIAjiQCIMneLn4uGOZ/EH1auD4hEkK2bxMH/f9/ImvP5BKwDgvZvO9j9QgSELCkEQBBEZop6+LQ78skJt/lk8+jLH7ZLRcPHEDP06KMxnW5xEBRIoBEEQBOGDkwIcgg3Ft5aJYDEJesSyeMyjDor331GCBApBEARBFJEg6byZNOMQS93nYJEpFSRQCIIgCMIHWyS4XDw5tBXExWNaYhaPYjtG9SRiMfWGXj6eiEEChSAIgogkUZobxhL+nw+BLCiCi0d1SXrSpvO5zKtQm8/fUYIECkEQBBFJolbZFIDWiO4bYuIzT45YmM0SF0hIMgIlEY8prx3FoBAEQRBEnkStcJiMXLKO/FKLWSwIc/Eodk2mWRePoRZBFINCEARBEPkRJX3iVJIVBnRZ5dsgkwXqHFfnOvSkTO5vZQgKWVAIgiAIIj9KbUGRWSHcdVDCaVeFeA1Ue7IuHksIrPXaP8L6hAQKQRAEER3Ywmelfrs3JbEf4QTJalSP5Y7KrpPvywbJiqnJ3LEF802pRaAXJFAIgiCISFLq+IhCZRH5zr8jzmbM1kFR7MNaUMwAxd1EwRIlSKAQBEEQkaTUY6fs8GGIFj+rhclZUHiZpkwzTmm6eITlQQJ2iw0JFIIgCCKSlNr9wLlanCDZcNvlljvreR+PTqG2pOjiUdZBIRcPQRAEQeRFqcdOU+Ja0emTLLNH1a4MdnXGXcO6fBQxKCl+G906KGlTvl0UIIFCEARBRJIoVZJVIZvN2A+VW2VfVwr/95+3BWuI2l3DwmfxeBSDE/sS4WucKHUHCIIg+ia5zMJC+MFe1VKHRxRq7PY6rz8+8y7ePXww1wedbrAxKOL8PfyxBRdPqS+yB2RBIQiCyInoPtgPFEptQeFcPIquyGSqX/aR33m9v6uDaUseCyMiWlB0j01BsgRBEAShATtclnrs5LJnFKIjl0JtfjEoPULRNb5PenVQVMcQY06i7OIhgUIQBJET5OIpBFzNj5Jn8ZSmDkoyJVpDgs3F45VmLAoXsqAQBEEcAPADVnQf7H0ZnZTaYmFquFZksFk8ezt7sOjtbUil+RgRL3pcAa/+/egRRI3qCKIgIYFCEARxABBha/gBA+vCKHmNDp2UYh9D2qf/sASX3fky/vTc+myzPu12p/LM4oFHFo+wuOTX2AMSKARBEJrwj3Jy8RQCk4mRKPXLfRil9t/Z1g4A+M/rW5xlfqLAlTLMungUfUoKFhrVtRNjTsiCQhAEcQAQ5bfNAwW+rHups3iyn/PtSUUiO9z6unhSojWE6YfKxePK4pFvSDEoBEEQByCkTwqPTmpvsdAJ2PWrGmtTkYg7n02f6q2sZjAtYS4exT5i7I56Lh4qdU8QBHHAUerZdT8UCINzKQnz6EEsKFwfhCBZVafYgmuWpb5TXWnGB1Kp+2eeeQbnnHMOWlpaYBgGHnzwQW79JZdcAsMwuH9nnnkmt83u3bsxa9Ys1NXVoaGhAZdddhna29vzOhGCIIhCE+GXzQMGSyOltliI1hyZFUW3Dkp5jgJFVCQq6SFmHKmO4aokW+qL7EFggdLR0YFJkyZh/vz5ym3OPPNMbNmyxfl37733cutnzZqFN998EwsXLsTDDz+MZ555BldccUXw3hMEQRQR/lke3Qd7X0Z0b5QUzm2il02jgregBOiCpWe54yc2PDDqoASei2fmzJmYOXOm5zYVFRVobm6Wrnv77bfx2GOP4aWXXsIxxxwDAPjNb36Ds846Cz/72c/Q0tIStEsEQRBFgVw8hUecybeUcFVtTblg0s3lYmNQggT/ivGuOsLDND22c7l4ontPFyQG5emnn0ZTUxPGjBmDr3zlK9i1a5ezbsmSJWhoaHDECQBMnz4dsVgMy5Ytk7bX3d2NtrY27h9BEESx4R/6lGZcCEydgIsiwQ36qtRdTR9PRVluFhTdIFmXBUUzi6fkVioPQhcoZ555Ju666y4sWrQIP/nJT7B48WLMnDkT6XQaALB161Y0NTVx+yQSCTQ2NmLr1q3SNufNm4f6+nrn37Bhw8LuNkEQhC9RGjwPVDirRaktKJpxHTpUljFZPIGDZP2ziXjXmNqC0pfqoAR28fjx2c9+1vk8YcIETJw4EYcccgiefvppnHbaaTm1OXfuXMyZM8f5u62tjUQKQRBFJ7qP8gMHS7BalBJeLFl59Se/GBR5n1i4vnkIFFMsdf9hsqCIjBo1CgMHDsTatWsBAM3Nzdi+fTu3TSqVwu7du5VxKxUVFairq+P+EQRBFBty8RQenaJkxYKzXEAhLDQ7GWNcQaJI8OyDEPCqrm8i7KOR7RO0L8Wm4AJl06ZN2LVrF4YMGQIAmDp1Kvbu3Yvly5c72zz55JMwTRNTpkwpdHcIgiByptSVTT8MiHEfpYQP2JVbUKQ9lGjXXC1D2hYUk21fL5gWiHYdlMAunvb2dscaAgDr16/HypUr0djYiMbGRtx444244IIL0NzcjHXr1uFb3/oWDj30UMyYMQMAcPjhh+PMM8/E5Zdfjt///vdIJpO46qqr8NnPfpYyeAiCiDSkTwpPtCwo2c+mBViSwVy3j7L06bK4gWTauwGx/X37k3h9UysmDK3nlrOuGsvDHSVaTEotAr0IbEF5+eWXcdRRR+Goo44CAMyZMwdHHXUUrrvuOsTjcbz22ms499xzMXr0aFx22WWYPHkynn32WVRUVDht3H333Rg7dixOO+00nHXWWTjxxBPxxz/+MbyzIgiCKADRfZQfOHDWglILFPCDvixeQzf1XFbCPxHzH4IzLp7svu/u7MA5v30Oz67ZwW/Hh6B4xKrwfx9QQbLTpk3zNHM+/vjjvm00NjbinnvuCXpogiCIkhLlt82+REd3Cn9Z+j5mHNGMkQOruXVRCpLlrB6mwsWThwUlEfOPYxJdPDbf+efr+OaMMTjvqIO4Nu1jabt4InxP01w8BEEQmlAl2XD46WOrcNOjqzD9F4td68S4j1LiDpLNvT+WICAAIBHXECjOf3g+2Lsf1/xtJdbv7OhtU/SNadZBibAFhQQKQTCkTQvX/m0lbn9+fam7QkQQ1pwf4ed65HnxvT0A5O4FUxAFpUSsySLTJ1LNIlkmC/5NxDVcPJY6IwcAVry/B1f+ZTnuX/EBd3jV/SkKlFSEb+TQ66AQRF9m0dvb8MArH+CBVz7ApSeMLHV3iIjBv6RG98Hel+FjUCJkQbEsqaDS7aE4mR8AlGm4eLzcNQDw40fewp7OpLCPx1w8pvh3dO9jsqAQBENHT6rUXSAijBiISOSG17AsxlKUEq00Y5kSkJygzIIS13DxwGeSQlGcZPqkFneuSrIRFtokUAiCwaDiW4QHfFZHCTvSx/GaviZSacbMZz9Lhm9bzL62JaZMy8UTfJJKVWAtIKkkW2oV6AEJFIJg0Jz3i/iQIsvEIMIlWlk8vCDNL4vHbRkq00ozDo5XWX5XJdkI38ckUAiCwSCFQnhQ6piIAwVdC0qpB0+3i0eyTU51UHpdPLppxrlcBt00Y7KgEETfgOQJ4UWU3A99GS9XapQyucWgaGmQbB51UMo0YlBMK6iDx3ufvlTqngQKQTCQAYXQpdRv9wcqUQqSdRc/yz2LR14HRTMGJeB18NrngC51TxAHMhQkS3ghK1dOBKevuHhYlC4eSR9lp8daX8wgLh7PwvWqfdTXri+VuieBQhAMZEEhvODTjKP7YI86Xj8zsXprKRFdevkIJmkdFJ004xwsKEFcPFESgSIkUAiCQeOFhogA7+3sQLIEznO+iFjRD3/g4PEmwKf2ljhIVpwsMK9CbW4Lit5kgTkINY86KBQkSxB9FlIoUWfhW9sw7WdP4/N/Xlb0Y5OLp/DIsl1KBR/Yql/WXrqZpA6KzmSBmaqwwV08agsK/zcJFILoI7AvdlEuAf1h5q4l7wEAlr67u+jHJhdPOHi7eOSfS4FYk0VaB0WzLZm41S3UFhQvUSMKklJbqbwggUIQDOyDM8o/XKJUkAUlDLxivUSrRSnh3U2qQm26dVDYz70WFM3ZjINehiBZPGRBIYg+AluoLcK/W6JEUCXZcPC0oIC3WpQSPmBXVajNjez85DEoOoXavOfike7jMX+Py8UT4duYBApBMJAFhfCCJgsMB8+KzZFy8fCf8yl1n3MdFAS/10xTvY84OWCUXdkkUAiCgYtBKfXTkYgcNFlg4SlFkOzezh589OdP45cL3+GWcy4e05IO5tql7nuTzthz0kkztnIIkgXUzy+xLXLxEEQfIcYolCj/cD/MlFIYmExmM90duaMbJKvzE9za2oVUninntz23Hu/u6MCvFq3hlpsmb/WQF2rTO4YtGNg2dOfiCYqXW0gULt2pdPADFAkSKATBwllQStcNIpqIdTGI3NANkvWzTix9dxeOn7cIl9z+Ul79SSp+7GJNFmmpe22Bkm3HRqsOikfAq/exVFk8/N8d3SRQCKJPwD43aQCKJqVM741SCuyBCh8k673tX5e+DwB4bu3O/I6pOI64XIzfCHYM24IS0MWD4Pe8V5Cs+Fzr6E4FaruYkEAhCAaDXDyEB1QHJRw8ZzPmRKD3NS7XCDLNB3cdlNzbclw8jAVDb7LA4Ae1FO4oth827T0kUAiiTyCLtCcIG+7tPsLT1Ecez8kC9QORyxPhDGEql5Po4gmjDgpnQdGqJJubi0cloEUXj66lpRSQQCEIBvYnGYUfKBEtKM248ASpNROWQNFx8WSEgiyLRw9T4uLRsaDAw12Tyz59KTuRBApBsDC/3Xz8zUThKOXXUmgB296dwjm/eQ6/FrJJDjT0C7V5t6NTKj4fxJRnWbKQ7DaQddveLpcsnqDuxIwFRdWeXltRePyRQCEIhiAPR+LDR6HfPu9Z9j5e/6AVvxDqcRxoeNZpCxCDEpZA0XHxKAu1ac4WaO8buA4KgosFr9opui9eUXj8kUAhCAY2riDKFRaJ0sCb/MO/P3pSFNjCpRkXKQZFJw7DtBSF2qT6xL1QVgdFO83Ydyvx+Opz0i0ZEwUXNwkUgmAQg+IIgkc/gLMUvLezA8+u2VHqbvjilcUD8KLAiwpGoORbrM27JxlhsbO923MbZ5nUgmL/n41B0QmSDa5QTNNSuoW0XTzBDlkQSKAQBANl8USfklaSjXiQ7LSfPY3P//lFrNy4t9Rd8cSzUFuAar1smnF3HtYnpYtHsKBs3LNfso1kP0lbtvXFFigxw2dOIqat4HVQvCwoFINCEH0S9jcZpTootz69Dg+t/KDU3fjQU2gXT1i8vmlvqbvgifdcgfoWFDaGoyuZe0VUnSweywI27u6UbYX/vL4FtzzxjiNoZFYKsQ5KzDAQ1xEoOdW696qDottE6e/vRKk7QBBRIkiAXrF444NW/OSxVQCAjx95UIl78+GGuycKcHvovFEf6PBpxt7bsqu7ChC/Iz4PNu6RCRTgq3evAACMGFCN8446SG5BEVw8McOAboxv8Doo6iBZe3llWQxdSfU1i8LjjywoBMHA/qijkma8p7On1F2IFKV8s4u6i6evoFtJ1m+UZL+P7jwsKOr2eZfvxt3eLp5H39jiWpbdTnDxxDRdPLkGySrW2cevqYi+fYIECkEwcEFxlFDRZ+noThUkC4smCwwH7zRj/Tgwdlsva0AutHenuOdB6/4kWvcn3X1gPi99d7drmY19LnaXdV08XtYQFZaHQkn3Lq/2EShRuL0DC5RnnnkG55xzDlpaWmAYBh588EFnXTKZxLe//W1MmDAB1dXVaGlpwRe+8AVs3ryZa2PEiBEwDIP7d9NNN+V9MgSRL30lxuDDjN/XsqV1P464/nFc+P+WFuDg2Y8RClFyEeGu+RIkk45d35UKz4Jy8+OrMP76x7HwrW3OMjv+ZEB1ObctKx5a9yczsTBeMSici6cwFhRVWX72+P3KfQRKBO6iwAKlo6MDkyZNwvz5813rOjs7sWLFCvzgBz/AihUrcP/992P16tU499xzXdv+8Ic/xJYtW5x/V199dW5nQBAhEiRAj4gm/1qZeSFatn536G3zLh66P3LFy7URZC4e9vvIJ0hWZP5T6wAA/1i+yVnW1pWZVK+uqozb1gJQzyzb15WSitdsDErm/4bhbUli2w9cqA3+lWQTMQNVZXF1GxG4vQM7oWbOnImZM2dK19XX12PhwoXcst/+9rc47rjjsGHDBgwfPtxZXltbi+bm5qCHJ4iCEiRAr1h414wgignv4ilhRw4gLMviBEuQuXjY9fmkGetgH0sUFZYliCrIa5DY29jZgQZ0S93nIIUt/zRjwwCqK+LYrxB2Ubi9Cx6D0traCsMw0NDQwC2/6aabMGDAABx11FG4+eabkUqpp3zu7u5GW1sb948gCgEXJBsVhUJEBjHtlMgNdlgWf2YBYmS59YUIkmWxnwcxQaGIAsJSiAPRxZOIx/TSjO1GA2Ba6kJtdj07A95unijEWBU0jLerqwvf/va3ceGFF6Kurs5Z/rWvfQ1HH300Ghsb8cILL2Du3LnYsmULfvGLX0jbmTdvHm688cZCdpUgXEThB0q4KeW3Ik4eFzYflixj9jxNy0IcBve3jZ/tgH2JCDtIVnUs0eghumAyQa3u/e2u2u3EY4ZmFk/w+8yC2gJsOZYgHxdP4KOGT8EESjKZxKc//WlYloVbb72VWzdnzhzn88SJE1FeXo4vf/nLmDdvHioqKlxtzZ07l9unra0Nw4YNK1TXiQ8x/IOmdP1QIZrDieJiKT6H1n4E77kgbN/XhavueQWzpgzXrtmTNi1w42SA3yAXJFtgC0qqN61PtKBAdPEoglrtvtoCJRErbJCs6mZKOwLF28UUhXuxIALFFifvv/8+nnzySc56ImPKlClIpVJ47733MGbMGNf6iooKqXAhiLBh39ii4uJhn4eW9eF5y44kfcTFU6q+3fSfVXhx/W68uH63p0Bhb2Gxr3ztEX5lVzKNSkbNcHVQChyDko3dEFw8cMfNSCvJmha6kmmkGFeRTqG2nIJkPUSNE6SLTC0WzwOXmNBjUGxxsmbNGjzxxBMYMGCA7z4rV65ELBZDU1NT2N0hiEBw84BEcASizKLS4jV4hkFfF597JXVCZPBBsfx1VMX5fP/B1zH2B49h1dY2Zn3xLChscKuITvD0e7s6ccT1j2N7WxeAzESB+oXagt9rqjpArItHJwamlAS2oLS3t2Pt2rXO3+vXr8fKlSvR2NiIIUOG4JOf/CRWrFiBhx9+GOl0Glu3bgUANDY2ory8HEuWLMGyZctw6qmnora2FkuWLMG1116Lz33uc+jfv394Z0YQOcD+pCNiQOGIYJeKTwkvQoEr3fd5chH1YsVmVZzPX5duAAD85sm1mH/R0a5tCx2DYj8PRKuDZVl8+rlPBs0Dr2Tm1IrHNOfigTymRbe/sj4A/pMVRiGNPrBAefnll3Hqqac6f9uxIRdffDFuuOEG/Otf/wIAHHnkkdx+Tz31FKZNm4aKigosWLAAN9xwA7q7uzFy5Ehce+21XIwJQZSKKJa6ZyELSmkJkmGSC309pVz3knAuHkFX+F1j1jLA1UEJsVCbF64sHojVb9UZNPZ6AIhrFmozPQSP937ynZx0aRiIGeqGo/CoCSxQpk2b5qmS/RT00UcfjaVLC1DhkSBCgLegROAXKhDBLhWdUr7ZWYq3+6gR5b4B7iweFr9A9RQrUJjP3QW2oNi4YlAst2XN6/Lbab6ZLB6NA+ZSBwVeAqX3g1+QbA7HDBuai4cgGLg3oQj6eCI+7hzw0GSB3ujen+x2oqVStEa495WvL54FRegPLFdsktfLTbYOiq6LJ0fXmUKvmboungg8bKI/nSFBFJHIpxnTsFhi+oYFJep4WSotxWebtMrFU+AgWRtxSBczZvwqvwZ18eR6m+m4eOJekzbmdthQIQsKQTBE0cXjVXUzKqRUr2sHGH0lSDbKfQNESyW/zq8YHufiKUAWj5/wlMegsPvD8wsoRqE2QG0BtmczNgzvNOMoPP5IoBAEA2dBiaAaiOJb+1ub2zDu+sfxm0VrinK8Ul4Cs8D3B1/zJnrftR+6Pfaab0esyure15Juu78nd4HCTxLqva2s1D3fP+/r4FhQNAu15R4kq1jO1GFxFZ1jiIK1lgQKQTAEeVCVgij26YcPv4melImfL3yn1F0pOFy9i0IfK4LftR+6osqrnoxKgNikFRaUzjwECovfOYhjunsuIXmhNmd7JkhWOwYlh7tNlYXITnroJVAioE9IoBAEC/uwiWKacRTfqvt6amwQuMtf4K8iKi7GQiO6eLh1kkvAbs9eI9WsvDoY3FxA3tuKg7pYcdo09SwoiVhMK4vHsnKsgyL0yzbWsIu9DDhRuPtIoBAES8TTSCPYpT5f/TQIha4ky5JP62F17alV2/Hwa5vDaYxB34LiPhH2xYENfcrHgsJbTn1iUHxGTdVkgWL7MU0XT6Z/wRHPwxZWrIvnQzkXD0H0VaIYJMsaKCLTJ4ZiC5SoXIFC9IMPiC7tmVqWhUvveAkAcNyIRjTVVWrso9t29rM7zZj5LNmXtViwAiafGJQg6FgMvQu1Zf4faLLAECr0xmIGYFqciyfqlWTJgkIQDNyDMyqJKSFmjpimhW//4zX8+bn1ebaU5cPq4im0fgjaftgWP9YV0NalN8eO7qDmZSXxC1RXWV86e1Jax5bBu3iCxaCI+FlQ2CwezxgQtj3frST7KV08vQIFPi6e0usTsqAQBEsxTfi6hGnVeWHdLvzt5Y0AgMtOHJlXWzZRc/EU8lvjBtYCv2EG/a7Dvl1TTLCHTjpsEPgsHnGdd6C6qg5KeC4e7239RIVf1o0tyOKG4SkQsn3LDfE8HBePlf2bKskSRB8iimnGYQZmtnfn/pZJFLeQX9D2w+4OG4yqK0+0RRJnqbRUq6QiUJXF050yXW3lgm8Mis/F8LNk2a6XeDxAobYcTku8FnFHoGi6eCLwgkYChSAYeGtFybrB4fdG+WGjlA9OS/lH+AS3oITbITaGQccVkemDXttelkp+viPvfcX1uWbysC4ev3PQsaB4fXc9qYzyS2i6eLynHvTYT+iDIbh4AO805wjoExIoBMHiNw9IKYhk4C5D2Ob/KFNoFw9fqC3YvmHPE5ROZ1vRzTbRxUtk+BVqYyvJilaCXONQuPo2vjEoftfCOwbFFihxw8gErvq1FmaQLLKWsUwdlMDNFhUSKAShICpigHujLGE/VBT7GVfSa5CDi+eFdTvx3ze3Bj9UUAtKyFcmlzpAun1gt3LVEfGxoHgJmDAyefy+V/8gWe/r4AgU3UJtOQbJikH+McHFE/Mr1BYBKEiWIBg403NE/ClRjIthifgzLlSCvGnbXPT/lgEAlsz9KIbUV2kfK3AMSgGDZMPug2epe6jXAWKaMb8ujGqy/nPxeO9vmj4WlF7lkIgbvjVVMv3J7bsVz8Put339DHhbcKLwfkYWFIJgKGYQpC5RqEfgRZT1SdhxGX41OkRYQbljX3eg9kudxRNEn1iWhRv+9SaWrd+tvb1zHFehNvlnG1WQLJCHQAnwu/ezOvjFtNoWFL+5cPj29L9c2x0nXhtDyOLxc/FE4blDAoUgGKIY75HPoCVSiMnoohyDEvqgzQoUjbbT3EDsv30+918u1h0v0h4iQmTZ+t2444X3tNv2fBHwSVvzCrDN1cXjV72WxT9IVtOCou3iCXYf2y26XTzZ/mX+jn4lWRIoBMEQphgIi6CDohd8IbqQBEooregT5BqE/R2Kb5WX3P4iLvzjUuWglkrrD/JAfinlYd+ubJCsX9vtXcGCU71cqZxIk1hxVHVQgNyDZIMEGPtpisyp6cSgxPSCZAPeCGKsiY2YZgzfSrKlh2JQCIKB/VFHpZJsoTKLwnJhRdiAEvpDVrz8T6/eAQD4YO9+DO3fz7U9G8ehY9XIJ6U87HMNYkHRiaVg8ZqU069YoqrUPZB7mnGQFxM/i6GfxcPufjyml0XjV/jN3UF7P4WLx4lB8askW3qJQhYUglAQRQtKvqKCfbaGd37RVSihW1ACtscOpkEFb9C+h32unBDw2Taom8+r1olfnA8/WWD4Lh7/GBT/tnS+iXgsppe+HfB7FYNhneUxu3/2dj51UAIdtTCQQCEIBv7BGYWfKCDW1gyLsFw8USbsr1DVnOo4ScZNopMVY3oEgPoRuovHw1IhElSierkavTJ8xO3dLp5cLShhx6D4fxkZC4pmkGyAL9cuOqeczbh3uX8lWe1DFgwSKATBwD84S9cPlkJlFuVS50JGlGczDl2gBGyPHUxZsaIir3ijggoU722D1tPIq5KsRMSVxzNDWa4uniDX3TcGBXpfRTwW0xMoVrCv1lBZUESBAr8CfKVXKCRQCIKBC9CLwisEwg2SZbFCEmDRdfCEnyoZ9J5grSbJlP8F5zNxAh2qoC6efFNvRTiboJeLR2ZBkQiY6oo4gHAqyfrPxeMXg2Jp1StKxDTn4vGpTCsiTgpo45S6N7PbRX02YxIoBMEQzdmMC9OnvmpBCULYXqx8LCg9GiY5P/eGF16Dfi5wQsBH6AUtmc4HowsWFJ+ZhVlPmd1Ov/JMvsf+ntxUd5CXAP/JAnUtKHqzGftVphWxm3QXanNn8XiJrSg8/UigEAQDpRkHx4iwDSWXOKKfPLYKZ/3qWenbeNDWWLdOUsdnmIdADn2ywAAunqC3gNfvzO83KMsuKk/EnL9veeIdnP6Lxdjb2ROgP/rX3e9+v+zOl7FPI+06HjNgGIZW2nIuWTxuF0/vcsfFQ5VkCaJPwbt4StYNjjDTjAthISq6BSVAv3P5Dm99eh3e2tKGfyzfJDm0vEFVl9hBolvDxZNPxlbYt6tXxVaRwC4ej/uQn5DRp1+9lzTRO9CmTQu3PLEGa7a3458rPgjQH/b43tsGTalWYffZr1hbrnVQxJAne7l9rjGqJEsQfQz2wRkVhRIiB4RACUIepyizMKkumepasjEoPVoChbVaBOt82N9tYYNk5Z8B0VXlfWD7PBO9QbJdTJBsbaV+mS/+evlYUEK64e1r5lusTdNlZGN3T7x2Yr8Nw1sckQWFICIGb0GJwC8U4Q48fF2OvuniyWUAywXZWaneKpUCJaCLR3+Y9N45jG82iEAJOmZ7Z/Ew2/lcsmwWT6YDG/d0OusaqsoAAE+t2o6129t92pF/lhE03kZForfPGvokx1L3/E7xmLidEfk0Y6okSxAM+VTyLBRhphkHefj3BSzLe3AM+ytUXX+1BSW7PKgFJZ8g2VAsKAGCZGWHsyxLOQB6xUJZAY5r72pbUN7f1cmss/DKhj249I6XAADv3XS2Vv/DdmepiGu6eDKWXP3v03HxKNKMbQzfINnSPwDJgkIQDIUIIs0Xv7TLIHAWlLBekUro4pGdQViBzrLBVe3ikS/n66DoBMkybeZReTaMr5avN6J/bNk+D638AL9c+I5z/3rVOglizbDbKeu1Rmxp7XLWpUwLb2xu825AaEfWH5HQBIqmi+dPz63Hznb9gN+si0dcLgoUw2VVYSELCkFEjCDug2IRplUnyPwqupQyBCXzHfE9yKeWCItsHAru4sk9BiWfSrJh3LspzsUT3JVmWhbivd/N1xesBACceNhAHDui0TOdOsiszHY7ZZKRNm1a2vdmKcoL2BaUsARPFjtIVnDxCIfxqyQbBciCQhAMnAUlIgJFfHzn1VYBgoCL/ZDjBmK/9WHHoGhYSlg4F49GJVm+776b8/sq2skVnUJttzzxDs7/3fPSOXBkA/2uXksAK0Jcpe4ZHed3HvYxZAIlpXG9s+3oHzMsQWHHoGjNxxMAuzlVqXsb/0qypYcECkEw8OmPJewIQ5h9Yr0MoRVqC6WV3FDFPjifQz+evEUd4aJnQWHaDNj7IC4ZrfY0Mlv+uWITVmzYizclrhTZNXGqmWoKAj9rhn19E5KBNoiLNoi4ixnAvPMnaLetbqcwFpRsxVhvgSJWkq0qi3Pro/B+FligPPPMMzjnnHPQ0tICwzDw4IMPcusty8J1112HIUOGoKqqCtOnT8eaNWu4bXbv3o1Zs2ahrq4ODQ0NuOyyy9De7h1lTRDFIIounnzeqkW8ZoLNlVJaif1iH/Iy1weIQVFdSzbupCftP09MWO68MAIceRePfBvb2iGLr5Fd+2wtDnUsVBBBbm8qtaCYlva9GcTFE4sZuPC44Xj1ujP0GleQ6C2o4hUHkgvZOij8eYjXQgySrSzjO9Ing2Q7OjowadIkzJ8/X7r+pz/9KX7961/j97//PZYtW4bq6mrMmDEDXV3Z4KVZs2bhzTffxMKFC/Hwww/jmWeewRVXXJH7WRBESFgFGMDzhe1Fvv7xIMGAukTNSByWoJOnGctRfS9ckGwqoEUkjxiUUCwoGhYZ+/xkZfxl+2TLsLPbCS4eHxcev63t4pFZUPSjjANVku09VF1VfiGchYpBsVsTT1/m4uEFSvQsKIGv8MyZMzFz5kzpOsuycMstt+D73/8+Pv7xjwMA7rrrLgwePBgPPvggPvvZz+Ltt9/GY489hpdeegnHHHMMAOA3v/kNzjrrLPzsZz9DS0tLHqdDEPkR9kM+DCJfB6XYMSg+QbD5BZr6BWXK16suJR+DUthCbdwbb5GCZO23dJn4kl2rrItHLX6CBcmqY1CSaQuVmmVf/eKaWOy6P/ne9wUTKOKcO72Il8IQXDwugRJqr3IjVOPS+vXrsXXrVkyfPt1ZVl9fjylTpmDJkiUAgCVLlqChocERJwAwffp0xGIxLFu2TNpud3c32trauH8EUQg4a0VEFIpGKIA27ClFJQg4H2Rm6CDxBCKsaJNm8ahcHaosHraSrE6htjwEcj5l8qXtaVhQbAGRklgr7NmyWZEhi0ERf2c6Ljp7H3t1QpXFk4OLx08UhVaoza6DEnKgqkwEAvI6KOyxKxKCiycCz4dQBcrWrVsBAIMHD+aWDx482Fm3detWNDU1cesTiQQaGxudbUTmzZuH+vp659+wYcPC7DZBOERzNuMs+Q483KBTgBiUYos63yDZgN1hrQayCrlKF48qi4fJJAmcZhzwWgYpcKaDTqE2W9B5xaCwp2FfUy+Xio6LLu20nfl/ucTFkwpw/YJVkg2p1L1jQRHbz69du3t+hdoqy+KcFWjywf259VF4+vWJLJ65c+eitbXV+bdx48ZSd4k4QImiiyfMgYevg5JXUw7sQF5sUSc7Hm+uzz3uQ25B8XZ1uJYHLNQWJP5CRBSybV1JfPr3S/Dn59YHbCmDVpBs7/IeDxdPmlcorva8Ksn6TSFgX3e5BcXUjo8KVqgt+zkfrWJbUMRCbYk8ZyO0f4/i71sUPkcP78+JliOHNeDOLx7n/B2F97NQBUpzczMAYNu2bdzybdu2Oeuam5uxfft2bn0qlcLu3budbUQqKipQV1fH/SOIQhMVF0iYool94BciCDiMJjt7vKeq94sXyCcThnPx+BxbZ3kyYJoxe0b5xM+YloVfPbEGL763Gz96+K1A7ThtaGXxqC0oopUDYAI4PQSBjsXQdik5sxnnaUHxCtoVYa0O+Rg7VKXu850tWacOSswAjhvZyGUQGYaBU0YPwsED+vUuKf3zL1SBMnLkSDQ3N2PRokXOsra2NixbtgxTp04FAEydOhV79+7F8uXLnW2efPJJmKaJKVOmhNkdgggM/yZV+h8okF/gpKstM/cBUAXn4smzzSXrdmHcdY9j3n/eVm7j5wJgFwWeEZgZZ8OoJJtmBm4tC0qAImWuvnEnDry2aW+wBgTSGveKLUKkMSiWux17cOdFCN+2qVCg7Pdht+mUupeM6kEEeKA0Y1ag5GFCUQXJ5m1BUczFw/Z1wkH1qK8q8xRbUXj8Bb4S7e3tWLlyJVauXAkgExi7cuVKbNiwAYZh4JprrsGPf/xj/Otf/8Lrr7+OL3zhC2hpacF5550HADj88MNx5pln4vLLL8eLL76I559/HldddRU++9nPUgYPUXK4INKI+HjCSpsFhEJtoc1mnCVfgfJ/vcLkD8+8q7eD5HD5WFBkAy3ftny5ViVZDQuKlY8FhflsWpbvDL5+cLMZK7ax++jp4vGxoLjroLjbYPcFMhk6mfWZv3XqoHiJVe7r87nsbJthWFBEF0/eMSi9/3cXast+HjWoJtMHTmzx+0fh6Rc4zfjll1/Gqaee6vw9Z84cAMDFF1+MO+64A9/61rfQ0dGBK664Anv37sWJJ56Ixx57DJWVlc4+d999N6666iqcdtppiMViuOCCC/DrX/86hNMhiPzgB4gSdoSBf3bmaUEpQBAwb0HJr618YkaYRlR/+MIFhsqaDujiYQf57oCVZINeS1HI7ulMBmtAgL8WCsuRZ6G23n0lVimvSrKqGBR2M/u6OmnGCf9KspaljhnRcSvZhB2DImqrkYNq8OrGvTm3K7vGAG+pKe89aIxz8dj/twOZc+5CaAQWKNOmTfNUooZh4Ic//CF++MMfKrdpbGzEPffcE/TQBFFw+Id8BH6hEB7YAWe4FSmIQClhkKzsaPlYUNjrK9s38GSBAYNk87n/2D50JrNVa2sqcisoxllQFF2xjykVKKbMgmJHycqPIx7LUiy3j+dYUCRukQdf+YCJp8j0I6aweQQr1Ma6RQwEFcE2Yqn7wXUV+OjYwTh7whB87s/ykhs66NRBsQVdzHUuWQvKt/7xKr522mE4/+ihOfclX/pEFg9BFAvZW1qpCRLA5wcfJJtXUw6cGT3PNnVOjx+0ZK6FYO2xsC4eWZC0jqWEa49NM9YSKP6iQL1v9vPG3Z3O54Z+ZcEa6oV38cg7k/YQKLIYFBsvoSyzZojfsysGRRIku31fN156b4/yOKr++F32WEg+Hjuw127v+FEDMO/8CXlXqJW50QBeWJXH49yxM+v5Bt7b1Ynl72evXykggUIQDGFmzIRFkIenHwWJQWGDF/MUUFoCxed65CPo+CBVmUBRWVDk7bHl1nVK3edVBZe5Gmy8i2UBr2zYg0//YQle39Sq3R4XJCvRVpZlOdc6KZk5OFsHxS10vKxcsqBw8VKkBOuMLM3Y3V+9df5BstnP+YSLiIXa7LbyrbMSUwTJsu3KLCg27JJSzrMFkEAhCI4gpt5i4WcxCEKYGUF+7edC0L1lh/NKYfXDbzJFP1eHCJdmrGNB4dr03Zzfl9medS2ZloXzb30BL67fjc/+cYl2e35Bsmz/vAq18dlA7vbEYE5ZULh4/GyasdqCIuIlyINVkg2pUJvj4hH/zq99dQxK9nNF3D1RoW1hYS0tYZfhDwoJFIJgCNOdEhZhZvGwg0FYdV74uI18LSjBrAy5zp2jQjaYsqha05ssMGiQbEALCitQGMFgMpaOjh7/GZVt+KJ+7r74FaGzV4vpyqzlRda2rFaPuI3tOrMXy7J43P3xEijZz36XnfPw5BUk2xuoKqTP5FsHxcadxcNYUHqvlyzN2FDsUwryc3YRxAEGl8UTUoxGvuQT9CniZyHIBb/J+8JGFURpk49I4t0aareFe7m8PTYGpbvQMShgBUP+94xfkCx7LWQuHvtcREEptuWeLNB9DHEfVxaPlkBxL9u8dz86e9KC5dS7HXeQbG7E46KLR164LSiOi0cMkmVjUBIx17JsFg9cy0oFCRSCYGB/01GpJMuSd5pxAQq1cdesCIE7fi6cfKxgfsXJlC4eZR0UZrJAnToo3Jt87hYUP6GlAy9QvMWatgXFdItK91w87u9AvO8dF49jQfEfSWXX4SM3PQkAGDWwWtkfkbDSjOOCS0dM880V9WSB2c+2QOFcPOCFEtu3UkEuHoJgMPMYIApFkLc7PzgXQkgWonzcErlgcZ+9B86g3fEqIKa7H4tYbt1PLOSVIs0KBtOULg9CWDEoYuCvy6XjEYNiXz+3BYU/hk71Va/r8O7ODunxZcQkbpFcEAu1ZYNkc2vvqOENuPtLU5z+ib9vduZiqYtHYkEJeaLlwJAFhSAYolioLUzRlM5xAPYiTBePzv5+MTkyF4EuKR/Lg06sCbdccH141eIA8rP+sFv7xdLo4GdN4mNQ1NcqzcUoudtyVZKVHMPtFhJcPIngLh7Vb8k/SDb7OYxS93FBGMRzVAW/+sxRGD6gn9KCYkhcPHEfsUUWFIKIEkV2VwDA/p40vvyXl3Hfy/JZusMMkuWLvoV0fiFeMx0Xll+mlV8ashe+QbKKBlXLk8JrrN/1Yc/n6ntfwQtrd3pur+pDKu19jXTwq6prcgJFrw6KLAaF/fsXC9/htncEivBN2sfOFmrTcPGIwkjxXQSLQckdcS4e27USRBSwYkYsVe+OQcl+dirJSi0o7DISKAQRGTj3QZFcPHcueQ+Pv7kN3/zHa9L1YVZ/DePNWiTM/uns7pdxESSOQxRpfrM9B54s0KN9GeLai/6kX1GUPVc29iVXIcrNZixbzx5Psw6KaclqmmT7+utFa7h1KguKOFOyTh0UVyaQ4rr4iWRuzNYcv49oqXMtSwguHttLJc7N4wUrUBxXkaJUvTxIlt2CdzW51xcfEigEwcA+5IsVJLuns8dzPS+a8jsWV6gtNBdPlvzn4tHZRt8K4XWKW1r34+gfL3QmKAR0AkPlbamupSsGxecE8xHF7J6pELJ4Uj7Xgj3nIGnGolCwg4dlx0g5lhL3dWStMVpBsqLIydGCkksMyj+/8hHcOutoblnWxcMn+AYRBTIXjcrowbZb5szF4xeDQhYUgogMhQgi9cM3VTFUC0X4Lp4wY2S06qBw1V5lbTDbejT3u6fWYW9nEn9kZk7m3AtBsngUy1PpYC6efC4fu28ynX+QLGdB8bnOMmuEvJKsW4TaAkV2aew+iKtMk3cVaaUZCwdQicpCxKBUlsVxSFMNt0ycsM9uKogoSLAWFMNtAWGRxaDIxFaU0oxJoBAEg6X4XEo4AZBnWwWpg1ICq5ONbPDVFUwyS4y/C0zx1q0Kks0jiycovIsnf1Hr14a/2JIIFJkFxZn4T21BkQXJstvnUqhNDGB2lvucV65xGawYqC6PZ107grAIIlBinEDx3l82m7GskiwLxaAQRISwhIdpFOCzZPLrk19mRi5wLp58JwvU2MZvbqJ8MrG46qkyq4Di/HTTjP0EXND+tnYm8adn38X2ti5uXy8Xz23PrXdZdmT4BslqngufxWO5JpS0LSgyYSBOCsguZzdP5ODiUcWg+AkU6QR7GrDxItXMDNNOoTZHYOTWZrZUvXxbvg4KH68CsJVk3aKnVJBAIQgGbrAtkkDxe8gFKcPtRz5prOo2wxc93seTH1u6PqDNKe2T/aIOkpW3JwaP+llQgl697z74On78yNuY9adlXN9SHnVQfvjwW5j/1Drftv2K+vmJUXt/d6E2vq3ulNqCok4z5rcv17CgiMJDHYPiY0FRfPaDjRepYQWKkMUTJM2YFUv2biqXMduu12zGFINCEBElzKJoYaEbU6EDn8KZX1s2YaZBy0boe1/cgG/8/VXnrd/XgqIZJCuDd4HJ2pbvp1NJFgg/SPbpVdsBAGu2t3MXw0/UPrjyA9+2uSBZyXo/a5CYaWN/Fq+BVwxKNs2YR3Tx6FhQXFYYZeaVdzsxH7eIzn6sBUW0fARqUyImVLuz7dqzGcdlAkXRfikggUIQDIWwMPjh9wzgXRb59SnMlGAbtpl8Y1Bke8+9/3X8c8UmPLV6h2sbuevBe70XvlYDlUDRTDN2FSVz/a3TyyxV5dmBLoh4Xc9UTlXBB8nKroWPNchx8fCCURWDIrNoZEvae7t4dGJQxGuvjEHxDZLNrQ4K7+KJM8v5toKIAt6CEsDF41SSzS5zLC9UB4Ugogn3bIqgBSXfLnECJbQsnvBEjzgQstko9jq/OKF83HR+M/gGdvF4BMne++IGHPPjJ/D6plbPY3pRVZ59hOc7T5NIShAWIroBv+w1betK4uSfPsVt51hQpJlA8uJuriBZnVL3gmVEtG5lt/Nx8eQag6Jy8Qj1S3K1oDj6QiGbZLMZc+4kqQWFBApBRIYwrRVhEWbgrl8abS7wFo1wLSjb93U7n2sry3qPod4e8HcBeeEbRBzQgiLGoLDtz73/dezq6MF3H3jdtx0VVWXZN/Ggl76zJ+W53u86+ulbez074P9zxQfo7Elz2yU9sniAzDVzVZIVgm1tl4V3f9Tfhc5yG1lxMx1iiiBZl4tHu0VezDhZPIpRne13hWaaMbl4CCJChBnvoYvfS0qYMR58nZewXDzsoJ5vW/zf29q6nM/2wOFbiC0PN51fjI6qNXUMin/cAzsI5OPiCXrp125v91wfZC4eGdm5eLwFtpPFoxIoluU6OdPk+5TLZIHqGJQALp4cLSj9ysMJkpUFtKosKOxZOYXaJDVdyIJCEBHFzGNwKxRh9inX2Xq94GJQ8lQo4pvydkag2PPaWB7bA8I1Ctgdv8FU+datHOx4lSNrsyIPK0hVWfYRHvTeaO/2tqD4unh8Y1DcglI24GUrycrb2dneg78u28AtS1sWd81zqSQrK88v9ldGrlk8rAWFtXyJacaVZXF8+ZRRqK3wn8tXKlAUnWIndHQKtcVkFpTcBFghoNmMCYLD5+28BIQ5W3Ah5uLJJ+bDj62tWYFiDyhBKpwG7Y/fXDwqIaKbZiyzylSyAiWgomLfxPMRYzLyDZK1tRl7zjLrgFeQLABcdsdLWLV1H7eMLfhmGHpxG9ouHj+BEkIdFDZ2SObamTvzcIxuqsU37ntVu32/LCBWLEsryUYwBoUECkEw8O6U4igUv1L3YWYWFTpINuxLto2JQbHTjP0EUT79SfkIONU1U90rLhePZP/KRAx/Wfo+Nu7uDCwaq8qz4iao9UpVqMxpzycGRdvFw1lQ3Nt1exRqA+ASJ5lts9+t7iAqfnfKQm0Ky4pNrnEZrIunMhF3LRdPQ+e0pFk8im1ZC4pdIp+fy8fdD7KgEESE4Ae/knWDI5+6HiKsxyG8UvdM+3ln8fB/szEosrLnfnGsgS0oOcZd6C6322S/06ryOH7w4BsA+LlVdGBdBR0+Qa+uvvkMxHyBNZkQ9G5fFiQre7v3c/FI+8ZYUOK6AkWMYwloDbOJSQZ1HdgwGVZYNtdXAgAG11Uqj6OCczfZQbKK3dKS70EmRvhKsmRBIYjIUIg6IX74BslynyMYg8J8zjsGxVOgqGe95dvwfvNXHQvwDwwNOqiJs/za7dtWA4AXGX5WDRG2/se+rmACxdeC4lOoLbcYFPd2PWkTlhBTAmRcIqr7KePiyXzWHUPF/qpiUHzroHCF2vSODfCBvKxA+cLUEZg4tAFHDmvgts/ZgqLYUfZ9y2JQWNVDWTwEESFKkcXjBy+a8mvLb36VXAjTwiPSuj/pfLZN1H6F2PJx07EPca+5YUR0C7XZ27V1Zc9Lp8iYCtY6sVriCvHCT0z6xSv5u3jc28liUCwrc93Z7SYOrffMZkmblnPuubp41FYv71KysvlrdGBPhxWl5YkYjhvZ6MSFBEGWEqzqk+y8OOuTZH8q1EYQEYKzVhQtBsWbUNOMfQbgXAjXxcPv353MPlTtN16/WjX5xKD47avyiuimGdvttzPWjqBWE1l7APCXpe8H2ldVqMzGL6PJf7JAtwVFdao9KdM5Rl1lAg989QRPd1emgFvms+5bviuLR1WozefryHUAZ7dlBYrO9jrb+BV6k1pQOH3idvuU2sVDAoUgGHTdA6Hi8xBg+9GVTOOBVzZhBxM8GgT2GRWWi8cv8yUI4t6sKyRb9ly9vbhMtzv3r9iEPR09XMaJ7FxUQkSdxZNp0LYG2O2z7hjRDRSEfL5DXwuKZpaOcr1TByW7TDWLck/KdI5XnoghHjM8Y0tMM+sS0ragCOejdNcFqIOSK5XlGgJFox2ZOFN1T+bS4lw80hgUjU4UEIpBIQgFUamDwoqmXy1aAwAY1liFZ7/10cBt+QU+5kKYbjHxknenslVHnSwEHxcOL2D0OjTn76/iyGENOH3cYGeZbIBWx6B4W1AqEjF09qSd68/WIOlKpqX76pDPdxgkBiVIPI6NvZq9jklhkLTjTHrSpiN4bAEQ96htwgbJ6uoF8ftUxaD4XZdcC7WxHNRQ5buNXpCsexu1i0dmQZHsTxYUgogmhUjDzRfZOLBx937n80MrP8C8R9/WckkVZLJArk5LuNesi3PxBJ/NmP0Kn1q1Hdc99AYnelhWbtzLDVqWZSGZNnHDv97Ewre2Acg9BsWOL7C328fEoLDnGJR8btEgMShyd5emi4dpR3Sr2JPW9aRMZ3tHoHhZUCzL+Z51K6+6ZjMOmJFlw1df1Tq0w+8/dzR+9PEjMHpwre+2Om3LtlGJCpmljj0X2T1MacYEESHCjPfQxe8Z4Cckvr5gJQDgI4cMxCmjB2m3lYdnQWhT/jkMWDHhpBkz64MEyf7yiXfw2qZWzDiiGSccOlB6vDR3fSz8c/km3PHCe7jjhffw3k1nawfD2se2+2wHwtrbtTEuHpVg0iEfF4/K3WLDp1y71/uJUVmQrGidKE/EsD+ZRnfKdLazBYeX8DDNbBaPOCAnYobUCuKeLFBfbMaM7PnwQbLBRvAzxw/R3lbPxeNtAWHxy+KBc37e7RcTsqAQBEOYAZ9hoduN3R3+cSm+k+HlQohxLaI1ho1BkU0q5xe8ya7e3ztJnZcgEAflzUwlW8DLgpKZ2+a0nz+NB1/5wLVthWBBYYNku/OxoBTQxcOVupfYqvwErixIVrx+ZTILSu+o5Bckm3Xx8NuphE3asvDUqu346M+exooNewKJTTZFuFiFzHK1oKhL3cssKNmN7bPmYlBKrBBIoBAEAzdAFumY7ANFNuDq9kP2vN3Z3q3cJrQsnhBdPOzuybQl7S9nIfFpg+2b/YBWxR4AYiVZy/UWa+8qDoKWZeGb/3gV63Z04Jq/rXS15Xbx6FtQvAKi8xGZupVgAf+aMTLse4GzoAjX3hZuPWm3iyfmm2aM3u35dSqBYlkWLr3jJby7swMX//nFQHPxJJh4mOJZFfyPI+vLsP79pNteecohAIBPTh7K7J9dL4vpCWohCpvQBcqIESNgGIbr3+zZswEA06ZNc6278sorw+4GQeQE764ovgVF9szPtR+3PPEOjvnxE1z6KWvhCOv8wrxm7N7iwJ0RLHz7ssOpBlY7QNNrYBVjdMTnv22xKBdql6RNi7OKAIJAcVw8mb/bu/VjUI793ydw/4pN0nX5uOkCFWqTCWddFw8XJCvEoCSyFhR7lR174mVBYefiEQdp1V7s6e7rTgWKQWH7wqfmFg6d0BqZVrr0hJHSmipHtNThzRtn4OZPTmSOwVhQJJej1DEooQuUl156CVu2bHH+LVy4EADwqU99ytnm8ssv57b56U9/GnY3CCI3ChRP0ZVM4z+vb+EKdMmQDvA59uOWJzIZP3YZdaAwLh4uKLV3kHl/VweeXr09h7ayn8WBO5U2XQ9RP4uTyQmU3jlfPM6bvT5ehdrEAcC03O2ypeRtS4G9P3tuXRoxKHP+/qp0eT4Wq7wLtfkc296fD5Ll95EGyWrEoLyycS9e2bg3s72wmXqyPP7Yyrl4JKIvEWddPO7aI4UgaB0Um+b6SmUsWnVFgttH6uJRrC8FoQfJDhrEX5ibbroJhxxyCE455RRnWb9+/dDc3Bz2oQkibwqVkXLjv9/EvS9uxAmHDsDdXzqeW8eaUXNJ57RRbca7kLKfw3LxyGqrnHLz0wCAv11xPKaMGhCgtWxjYvptyrRc18IveJP9bA9IXuctDsqiids+foUgUCzLkkxGlx3p7FgLe3/WkpBPDIp9vcvihiuF1w8vVxfgX7HX7/bJuniyy5QWlHQaMSNTG8QWHF4C5dk1O/Hsmp2Z7TWzeFyzGQdIGY+XwIKi07Zqm+GNbjeP7HTZ83JcPMz6UguUgsag9PT04K9//Su++MUvcqrs7rvvxsCBAzF+/HjMnTsXnZ2dnu10d3ejra2N+0cQhYAPkg2v3b+9tBEA8PzaXdrHd5bleWz2IcNbCPJsuBe2f6KoW7Fhb7C2mN2/c/9r3Lpk2nRdixv//SY27eGfH6pMrGSKj0GRXVevOjGWZSktKGnLPZeMLYjiMcMZCOxBoCcVzIKiwu5PLuXy/Uq6+9ZB8Y1h6W2HFYmCKMq6eNxZOXHNCE1dF494CmnFD0AmYMtiCqtCiYNkVQzr719nBRCsT5b7uKUu1FZQgfLggw9i7969uOSSS5xlF110Ef7617/iqaeewty5c/GXv/wFn/vc5zzbmTdvHurr651/w4YNK2S3iQ8xvHsgPIWiawqWHVO3G6rN2IdMWmFdyAtF3ZHM37kfQxRzqbTbgrJq6z5cevtLymNyQbK9A7KnBcUjBqUnzVc75Y+pTmNlBYp97B5mcMynUJvd3aCzILP9U+FXb0Y3yJa3YinqoDDXNqYRg8Kie+raLh5pkKwii0fv0AVDde5DJYGyVZLqtexzSWZBOaDn4vnzn/+MmTNnoqWlxVl2xRVXYMaMGZgwYQJmzZqFu+66Cw888ADWrVunbGfu3LlobW11/m3cuLGQ3e6zrNy4F2f96lm8sHZnqbvSZ8lnHheWb/3jVcy+e4WWCGCfAfkEyaqOZXCBcIwFpQhBsjc/vhpzerNa8iVpumNQAGDN9nbub3UMSq/LweO8ubmKhM16UqYySNaUuJ/sGJSExILCuXhSeqas1k53/FJ+FhR9gSK78LL7csrIRkw+uH/vevdxZHVQAP7a2tdK13XjckMYwJPfOAW1lXwEg+v7UaWM+wbJFicGRceCq3LBDGNcPN8/+3A8fPWJqPSZ/8e+PHwMin8fCknBBMr777+PJ554Al/60pc8t5syZQoAYO3atcptKioqUFdXx/0j3HzuT8vw1pY2XPSnZaXuSp9FJwjTj2TaxN9f3oRHXt+Cvb2Diu7vXGpB8dheSwAxn/2CQHOBm7xP0ub9r3zgGxycbUtNSkg7ViGLnTDNrHvGy3LA1f4Q0oyT6awbR4xBaetKumJAbGtBImY4D31blyS5irX+5wQAG/e4XeF2fxIeZeFV+MWs+AXJyu7VU8YMctwLTgyKh+jnBIoQJKttQZFsN2pQDT5yCB/75HLxKC68NIsnzoqS7PJCjt86LyYn9wbDjhpYzS0fyrh4RgyoxviD6n3bso8WpRiUglWSvf3229HU1ISzzz7bc7uVK1cCAIYM0a+wR8hh5/cgckN8JJgWEPTZz74d6zxkODew7GXaowluMFZsE+PMuOy+IQkUrk3/bbzb8hIPpt7cOpJU6iTjWlDFHgBwzfacZP7OvOVnPosWCztgk++vLR5izj1kt9+jaTVh2bSn0zXQ2NcrkUNFLa8YlEwaL/O35LrLvuuYYTiCQVbqXiQrULLzFOkEyXJtKKxH4uDqlWXFIg+SzR5DNRcPW202DHRePgbWVOC1G85wzY5cXZEd2nVfDmR1UEpdqK0gAsU0Tdx+++24+OKLkUhkD7Fu3Trcc889OOusszBgwAC89tpruPbaa3HyySdj4sSJHi0SRJFwZYlYiAd8T2LfTNOSH70XQbN4dEQGF6nPBYHq9ckPHdGjW/HUz4Ki04xMtLHBmU7JfElbfAwKLzbZt3xZnQlZfwFFkGwOEcrt3e5YlayLJ9wYFHcsEfDcmp0YPbgGTXWV3LFZYkZ2AM+6eNR9qGBiUOxrY9dB8ZqLh0X8Luy9RIEi3pvqNGP38jJFoTY2yyseM2AGzKTyQucWMQygrrJMuu7rpx2Gp1dvx4wj9DJms5enOC4sHQoiUJ544gls2LABX/ziF7nl5eXleOKJJ3DLLbego6MDw4YNwwUXXIDvf//7hegGQQQmjCBPdo6ToLsHDZLVcdNwLp4CxKBwcS2K/iQ11ZBXl1Kmu1CbtA1JqrjMqiUTTWIWT5KxdPSk08osHhmOeIgZzqCWlvRHF5nFw+5uIuQYFHHdU6u249an16E8HsM7/zsTgPwN34DhWEBkpe5FeBdPZpnj4tEUXSoLiqvInujiUQbJupepCrWxZL7j8ARKUOuryLWnj8a1p4/WPp4leZkqdRBwQQTKGWecIb15hw0bhsWLFxfikAQRCqIpO5cxnH0zsx+CmTct/8aCBslyq3rf+H/z5FqcdFh2Mjw+CJcfgMOA7cO8R1fhYxNbXNvoxrt4CZBk2tSzoDDjuN1cUmJBkQk0MbVWDGZ1LCgagsAWZfF41oLS3pXCTx9bhVcCpl9n+uZeZvcn7CweWbYUwFt+ZN+pwVhQ7Ca8vntnLp60lbuLRyEWXRYUoR/KOijSIFn/uXh0+6tLsStZ20f7UMSgEERfxB0kG7yNoDEofFCnxILisa/4kL176fv49aI1+PWiNc6yGOfiUe+bK6yoS5sWvnL3ctc2fkXBsm2pSaUtrRdUdhNZ1owdeyD7brggYsviBuQeZsbdIBaURCzmPOhvfny1b3qvuj23QgnSH9W+MnTuW9lXGjOyAcGmhxC0sa0kKdbFY1eS1RwcRfeWfXxRL+hm8ciWs+JD5fbQ7a8uRdYnTBZPdlmpBQpNFkhElhv+9Sbm/G1lqBVd/RAPlZuLh7VS9H7w+J3z87/494mFmy8FFtbt6HBtwxVq8zlWLoj9k1kHvAbl6x56A3P+3vs9SzazrRXdqTQuu/Ml9wau/rAunt7jSywosjdlMc28J5X9mxUoFQnvlE32mIlYNnA0V3Gi2tdx8YRsQdGxeMl+l5kYFL5vnkGyvd8tWyU4W6gtxxgUw+6LECQr1kFRiGb7N8IGnrL1W1RpxvEc4oC8CCvLThfZs+6ATTMmiHxImxbueOE93P/KB3hvl3el4TARf6I5CRQzmAVF9sbPrfd08fADquyFRzZjKRCei0frbVsRg9KVTOOuJe/j/hUfYHNrl/Rtu6Is85hasWEvXn5/j++x+KyizB89shgUSbfFNGwuSJZxMWkFydounpgROBNMhmzAMhkrTfD2vLKZcuuPYRhMkKyl3M7GtqCwkwUGFSiqGjBi+rF4a3X2yLMe7WvK1lFp3Z/NhFGVug/bglIsF8/AmnIAwJRRjQD4wN8DMkiWIPKFHRhSOQQU5oooBnIZw6VZPJrHDFoHhbegyI9jODEBfB2R8Oqg+KOqudHBpMbHDEWfAnZTdg1Z0egVgyLGD7HpwLwFRUeg9IqHuKFddMwL6eSFedRB8XK7ebllelImyhMxRZpxdgCX1UERKXMsKBIXT94xKPzf4n2x4CV5wU+xoi3AF9NTphmHbG4oluH4uW9/FJ09aTRWZ4TKh6bUPUHkSkoYeIuF66GQw8FTXBEuHVO5x/Hh/SblLs3ufqLYDxlxgAvtDU2jGZUY6mBSZ9OmuxqrZvPK7V9Ytwsd3SnuO7H7IvtueBeP5UozVpW6l5GtJBsL5e1a7uKxBUphs3hYbMuD7Lsy2BgUDRePLVCSqexki/b9quu2EsWiKs04bVlaosfuh2EAV516KEYPrsH5Rw3Ntq8Kkg3Z2hBWjJgflWVxR5wAhRVdQSGBQkSSfFJ180HM4sllEOczHTL/93p2mYLIcPXJowucO0MxEIh1KbJ9K56LRxXvwBYXTDGZHCxBY5DY/jy5ajsuvu1F7juxxYrUIuEVJJtWl7qXwVaSDSPY0MvFU55LJdkcg2Q7etLK/sRkLh6Pr88OcE2a7rl4dAdH0cVz2OBaAG73hGXpiYiUme3H/8wYg/9eewoa+mVrjbDtinVQwqTYWTw27DmV2oJCLh4iknClwItoQwknSNYd72B4OHn4QmeSPnkcS6d0vThgONuHlsXjj8pNxwoUVRpxYB0lbP/y+3v4wGUnNsK9q1jeXUwzDmJBYScLDONNlA3uvfvFDTisqYYJkg03BsXTgtLtZUGRBMl6ZfH09jvJFOGzB3pdC4otFh+++kTc/vx7+J8ZmdofLhePaWUqo/rMzSh1XSnqoBSy6qrOfV+QEBHu/CgGhSBccDEDIVZn9EN8mOZiZJDVQfGCm8smYJAsu33akj+wDMO9LVDcIFmVBYWNQVFNmhdUoMr6w8U09d5bsuu6fmc2INuUxKDYuwQq1BaPIQcPjAv7+/rDM+/iJ4+tQkUihoE1FQDCj0HRsaDI7p+YkR3M7evr6eLpvY4pxjqVDZLVu2h2G+MPqsfPPz2J6YvExaMx4LIuHhvlZIHMfqq2xw3Jbe64oO7hsIhSHRRy8RCRhH145lIWPFdcISg5PAGSEveU1++cj0EJ5uJhX4L9XDy6080HRecSqQbDdg2BErSbss2TkgJjMgvSzvZu5rgWZ8nrSmZfvXVmD06ype5DikExTQs/eWwVAL5wXFizGS9ZtwsX/nEp1mxrl+yRIWtBca/LxKCgd73alTZqYDUWf3MaynoH/mQ6G4Acc4Jk9c5DPRcP/7dp6bmNbAHLDs7s96f6KmVt/+SCCfjLZcf5HlNGsdOMbT4UsxkTRD6wg2cxLSji6JbLkVOSLB7PQ/rVQfHoBdt+yrSkriRlkGwxBYrCncBbUOS296+ccoh2X0zTkt4vXCVZp1CbvI3PHDMMgDvNmBVQehYUJgZF8aTXiWVh29va1uX8XVUWD72S7CW3v4gl7+7CpXeo68102jEo0roZTFn/3ssls8ZcMHkoDh5Q7QT3Zlw8tgUls42u20qZxeNKM9YLkpUWLFNYUNiNZN/BZ44djgG9Vq6gTBvTlNN++WJwn8mCQhAuUqVKMxb+DqsOitfPnI9BkQVCeu3Lu4fkdVB6a00I1zE8C4p/O6q3QT8Lyo/PG49zJrlL58swTQvnzn8OH+zd71rH3kNpH9fDoNrMgJIp1Jbdb39PVkAFSTOOewTJ9qvwL/jGtscKpoqymCMCVFk8XgOy7DtRWbFYOuwsHlkdFLAxKGoLit0vJ0hWNllg2LMZm3ouHllpAPYQ/ACuPl6+jBxYjRe+89FQ29RBlaVUCkigEJEkWSoXT8h1UHQGb754mqRPHhYUdpBIm5ZcCPUu7BEGH60y/KaFlRv3cu4Nd//8UdVB4QRK0n3yowZWa8/Uu31fN974oE26TjaHjOr8K3sLw4lZPPuTAQVKmo1BkZ+DTjs2piBQ0ozVQXWNvK6dTKA2985U7IVtQZFWHo2xc/HYrjR3G/blcOqgMEGyWRdP2HVQNF08aT4WBhAzW+TunrCzeACgpaHKc30hBATFoBCED6ULkhX+zkGhsH3Pphmrf+iyyqdefWLhZic2fSwoqeAWlLuXvY/z5j+PS29Xm/x1jEzqOijeLp54zNCu8dHenVSuk5W6V/WpsrfEuThZICtQ9NKM/S0oQbJvUqbFld5ny8Or2inzaF+WxTNqULVvP+zvTPbewM3F09tVeTCtbUGxJwtkYlB6L1XelWRdacZyC4q4yHmpUGXrKIJkc3Gz5UtBgmTZGJQSKwQSKEQkYd+4c5maPleCzGbckzLxp2ffxTvb9nHLk5KUVs9jcsXBpJ1SwgXJKo5lPzdFK0ZaQ/j9Zen7AIAl7+5S90Eri0eVZpwd9GUWlHjMcAIp/WDLkYvoThYIAFXlvQLF5K9ZF+PiCRqDotIzutahTHuWKxvJyRRKKARQQAtKdYV/YqeXBcUwsgHBOi4eZ7JAM5vFEzjNWPFdiC8FadOSBn1Wl/PnLHPxcLVPYvLlpS5qFhZkQSEIH9iYAa+CUmEjPnO93Ct/fm49fvzI2zjjl89wy/nJAvOPQfF08bgsKLIgWbkFRS+A13cTzToohbeg7OlQCxTWReXEoCg6XpnIWlC4GJSkvkAxTYsrda+KfQiSfSPGoKTM7NQFKkuJV/t+hepUdHhVkoW7DorsPrNFSDlbSdZ28TizEetaUOTbyVw8stPrV87HAWUtOXJLSdA040JS6DoopdZcJFCISMK+3SU1AvfCwl2oTb3tKxv2SJfzQbLu9eIg4FcHxasPfB0UeQyKLVrseAr7zVTHxaMjPvIJkvWrg5IRKHpPyT2dPcp1+7qyx3HSjBV9si0oYgzKo29sBZAZFPxcM2krm00Uj8WULr4gJerTJt8fy8pahlTXyEugyESjzj3R2e0Rg2IYWnVQ7EHevhdllWRzLXVvI7qITMuSiiXRamTfF6pgUXWhtgPFgsLG2JAFhSBcyAprFQN3kKz6ga1aI5sskFUOosvKt5KsRx+4yqfKOiiZ/9vWAHsaeZ34mrCKRSWVLh5vgRIzDM84Cpa9nWoLyj62pH7veavOzQ6SFbN4bOKGv2hKsxaUmKGMpwju4uH7bAsWlRDxDpJ1n5tOxpxtQZHHoLgLA3pZUOwia8m028UT17w2qnMXB1fTsqT3vI4FRRUwy5W6L3XKS0io4m1KAQkUIpLwhdqK6OIR//YSKIp1slL3LC4LisV+lrl41LDtp0xL6ksS04wrex/IoVlQNLbRSjOWZAol4urBXWTvfrUFpY2JT7FjQ1QuLtvFoyLmIThsTMvKxqB4nEOQwMq0abmsifYpqNrxstDIvhOde8K2eknTjLm5eKDczh7MbfGZYjKS7DFRd8APMpux1IIixKBIS91rpN4WIovHj0IEybKU2ihEpe6JSMJn8UTLxbOtrQuGZFublMSqwf7ORdO6X6E2bxcP89mnUFtSsKDY++RrmtYJklWlGfu6eAxD28qwx8uCInHxqAxzFWXeAiVuGL7CIs0UjPOaLFAn2NYmZZrKgHGVEPF08eQYg7J5b6ZYnP9cPGoLSsyxoPS6eNJZF0/QOii6WTymJT8/sRZN1sXDhcnK2/Vw8Xxlmn6BwSgRpSBZEihEJIlqFk93Ko0p/7cIAHDSYQOlbSTT3jEoomndbzZjr9ckcfZdr+eJbUFhBUrKtFDuNRDoBMn2brPw2pNx7m+f54JJs/3UyOKRuXhimbTVeMzwHTz3esSgtHWxFhQ7SFYRg+InUDQsKKyLJx5Tz8VT4WOtEdtU1QTKpQ6KLItLx4KyZvs+pE25NYKbzdhDCIrVYpPpbMCv4+IJuw6KKXfxiPFE2ViY7DJlDAqzH3upH5p9AiYNa/DqdigUpA5KhDxV5OIhIgk7iCfTFrqSafxlyXvYuLvTY6/8EZ+5d7ywHtuZ8uKtzFs6+1bOwpW6dwZDZr2HiydwkCxXqE2xTe8mScHFozre4nd2YNHb2zJ9Ux/awbYAxWKGE78hohr42MwdWTE421Kh4wrxyuJhXTz+dVC8H4sxnSBZ02ImC1RbUIIUapPFoNio+uN13WTfiY61sitpYuPuTqlujhl8bRNAfo+JWTwppg6KbbkIOpuxiCwGRSdzTTZZoDKLh1nBTm5YCndPWHBF6Up8HiRQiEgiWlB+++Ra/OChN3HazxcX9LjiM/veFzfis39cml1gsNvKH3b8ZIGZbTiXlXAQ9k+pAUUhEyzLkrh43Nj9tC0U/QQLCktXMo2Lb3sRl935Mtq6kpp1XDL/jxmG0iKgSjNWzXVjIxb08sIri6dN5uLxqYNiUx6P4dZZR2f7pJFZlGaKvHkVagvm4rGU1kS1BSX8NGMAWL1tn3TbmJEVqXZdG5mQdzJ1nFL3WUFnd1l3NmO1BcXt4pFZc4b256u1ZuugyLNZeOHCBMky3Si1ayQfVNaiUkAChYgkXOXPtIXn1+0EUIyy9+6H7rs7O7JrmdWyDA9AnsXDPszZt9SbH1+Ff67Y5PwtrYOiGDNMS6gkq3Dx2Md2sniYAVg083cyxcjYwmRe2C0YyMwPI0NlQWGvoawOij2A6aQae2bxSF088m3FINmyuMEN9DoxKKaZPY5XFo9K0H3j9NGuZWKhNr6P8uvuJYCkWTw+AsUuhf/O1n1ya4SRrcTblUpj055O6dxI2bl4el08pmwuHknzksuosqCIi02FW2pscy1+csEEnH/UQQBYwZ3dhnf3qCworOVB2qXQKUwl2eznUgstEihEJOFdPGbR5tT0+8GzD3CVWJLVQeFmZ+79/P6uDsx/ah23b5AgWdOy+PgVpnAXi70sKYlBER/YrJvFgt4DkM2+ULksZK6DVNrkzk1aSdYx93s/qt74oJWb6VekbX/uFpSyRMxJhwX0snjSFluoLaY0lasE3eUnj0KtUJ8jU+peFSSrnyXEFlITYzL8LChHtNQByFhQZBlnrBWtK2liyTp5BeKsZSzz/0xNF97FI7OgyM6nTNuCYknP75gR/fGZY4fjI4cKMWWKNGPFJsq05L6HXICVAhIoRCThXTzyCqmFwG88Zgda2YAKCKXuewPz2Ge5bR3a2uoeUOUWFHmvxLoOaYWP3W7THtxYESG+RbMWlGTaVLqXvnTny/j4/OeRZs4tY96XWwRkA4MYT6EKkgX864V87DfPea5nxaRfDIr4Rp6IxbjjZywoPjEoacu5VxIxdSVZrxgUsXdmDjEoMssKm/HzqT8s4e4vv5pDtkBZs61d4eLJiq6uZNqZIuHQphpuO9GCAmTTzMWZjmX7sejGoIjWoQHV5XjsmpNwaFNtZnvJuWQbkx5CcPEUX6AUOki21EKLBAoRKVJpE+/v6uBL3RfRguIXc6Ezy7JYB0V8MNqDgCwtVloHRWVBMQXXkcKCYosW24JSnog5b6LieLSfEyjy9na2d+OJt7fh1Y17sXnvfk7EKC0oksFMtAZIXTwBMzoO8pn9FcheM9V1Fa0diZjBDYJaWTwWm8VjKH35XvET4r0QVgwKa4VY/v4ezjXmNzHnEQfVAwDW7WhXxgxVOhaUNNb3ukcnDq3ntrO7xVp+7Pa8JguUCTGVQBF3F89tUG0FxjbXOX+LlhgNfaJ28YT4wCqlRiCBQhAMl935Mk65+Wn8p7esOFDsSrLe69m+qMzt7GDMZnM463sflLK0WNmpqqwYpiRIVvZWa5+T3d/yRNbl4LagMBVX06b0erCTI8ZihtOHjItHFSTrPjFR4HlbUPwfVfMvOhqHCG/qMvxK3QP8YBOP8TEosZh/hgn7vSdihtrFo7heqliioHVQZK4fceBPegRwixw8oB9qKhJImRbW7WgHwAsEA9ksqO6UiV3tmXt8cG/sirOdUKgts31GoNrXSnaNpRYUTRePeO2G1PN9EtPL+VgT/4E6XiAXT7ElAvubpyBZIhK8sHan0l9cTBa/swMA8OrGvc6ynpR3fY8w8SvtzlW4VQbJ8vOliCLAHgRkFpRgQbJ8DIpMDNnLgWxF3rK42oLSycSgqCxE72zNCpR02nJ8EZn4gwAWFFGgSFxmQdKMqyviWjMfOwLF47tmBxtZkCxbhv2ghipc8pEROHn0IGeZaWXdMYl4jGvvwuOG4SOHDMCCK45XXq+YYbhkacqrDoqylL67fXEZex/7xaCUxWMYPTgjAjfuzgS/sgLBYNx8XUkTu9q7AQCDaiq4duzrwcbz2ALVK+5Idh/o1kFhJx09angD/vcTE7j1okBRBcmqKJSLp/jz4WSvU6nn4qFCbQQ6ulO46E/LAACrfnSmMo6gVKRMU1ohtRD4xqDoBMkKWTxuC4rt4pFYUKRBsgoLiuDiMS1LKrDEGJTyRMx5mIriiXXxqMz9q7e1Z7cxs3EqBpPBISKNQREEXpfExZNNR/V/lyqLx7QsLfY5e4lR0XRfnmAzNPgsnps/OdEJsDzmx09gZ3t3r1jMphmzg9e4IXWYd/5EAJngXunxIalqbFpIpuR9VlloZK4fcfBkBYpfHZTyeAyHNtVgxYa90mPEmEDptq6k0/agWkGgMP1N9BbhswWq3T/ZXDwyC4rK3SZeE/bc7r38eNe9WlUuuniy+4sWIGcbg78vssulm+fEh9mCQgKF4OoUdKfM6AmUIs7FYysUVeVS9iGnetvks3jcMSj2fns63AIlyFw8UguKTKA4FpSMACiLZwWKKH5cQbKS9lgXT9rMupkMGKhmyoYf1FCFeMzAht2d0uBOHQuKV8CkSFk8ppWOnDYt/GLhO9jZrq6bUhaPOW/0ovCJG7zgYL9eW7iIkwWyAxbr1lFZUAzD7dpTlbr3iomRWSHEwZN1rfm5eMriMdRVlrmW2WSK9WXOzxHE8RhqK/mhhhVJ5b3XWnTxyCaJDDJ3kSjE2OeIzMLheu4xm4weXIv/+8QEDK6rUG3Cu3hKPbLnAfuTJwsKUXJUMQ5RoSdtFu01gq3FkJZcF51S4GIWjyhkbFOzrgVF9fW4BIrlnaZsv31XMEGy4vnsZ2JQkmn5nfHBnmxdi0xgbmarmAHUMoPXGUcMxkENVfjxI29LS93rBMlmzf3+N0BCcMWo2Nneg18vWuO5jVcMSjzGZ/Gw30GcFSiMi4ftPptaLItBMYzMwCB+l6oYlLihDsLVuR49AQRKIm64UqM5Fw/cA/2AmnKXgOIsKHHRxePextkvwOzPbhdP9jxlbXu5eADgoinDXfskhPvE+Ryqiye0prRgf/Wl1lkkUAi+1LpmJclikgopi6ejO4ULbn0B08Y04Tszx0q3sc8+FgMgqVOmY81hLSg/eOhNLF2/m1tvD9ayN3h5qXv5MdOWxcWQqOYasa0qtsWCLb0uno9oQWGP/beXNuB3T6/j6o2wFhQYQA1Tu4O11CQl/QoSJKvl4onFAr1he7bFDIQJMUjWZUFhHui9m7GuvUSMd1ByFhRJHRRnW+GSqWJQMhP0ySwCcouSuKQnbWLV1jZcftfLyrgqm7J4DOVxd6XdbF/c0x0MqCl39Y/9Ou1r62TxON+5RKAw7Xxy8lBcM/0wZV/Ft39W3Mluk37CrMY6buV+zP3O10Hx3TWy8C6e0p4IBckSrgnnokamDkr+7dz/ygdYtXUffr94nXIbtqaHtC8aGUWiO+OR17ZI1+/q6Hbta1oWtrTylTdV34hl8d9XyjQVQba8QClng2Q9XDxifMq3//k63t/Fz4XE3jsxw+BM+fGY4QgL2cR0bguK+tpquXgShpaQ0YG1kCTiMW4QFq8Z+5c9gJqm5Vy/uJDFw1tQ3P217z3RfqWqg6Iqpd+vPKG8j084dIDzuTtp4up7XnGCXr0oj8c8LSiZGBTBglJd4fr9sv2yBYpdJDDmYTVjheHEofUY2r+fsq8qF0/MkLsuXBYUjVuphnFpsrdemK6RYsXf2bB32AFXqO2GG26AYRjcv7Fjs2+rXV1dmD17NgYMGICamhpccMEF2LZtW9jdIALACZQIWlDCms04rdGOPSiofPqygVbEL9DQflDuklhQfrVoDabOexK3P7/eWaYMkhUKtWWCZmXbZf6fDZKNM2nGgouHzeJJyeugcOfCiBgD4OITEkwwqcx1IH6v3mm//o+qRIgWFPbtPR4zUMYEyYr9tDgLijwGhX3zZ0WJLANFNSikTMsVWAy4Y2Js+pXHlW/yd31xCsY2ZwqU9aTT2LTHX5wAGaEoiiqZdYkVlHILCitQeBdPVqDIYlD0J+RzuXjSfPsilR5BsipYiyErQkO1oHi0VYh4Qc4ieCBaUI444ghs2bLF+ffcc9kqj9deey3+/e9/47777sPixYuxefNmnH/++YXoBqEJO8hEVaCE8Rah81Zjn77q4adTk8XPj58yTZimxVkrbN7dkSlsdeO/33KWqUQC515Br8tHsrE4F09Z3GDSjMUYFN6C4nc39DBZJYZhoIaxoLCCQXbd/NwJLDrpw+WMSylf2EFXTDMWrYzsqbFBsrt7g6Ab+pVxb+PsoCKPQem1oGjGoGRiVtznUK2woBhG5v6u7h1cH3ltKydMvYjH3BNCslMD2Idj5zMaWFPh6gf7t231ylaStZd7W1D8xKi7DkqvBUWxX7kQK6QzNlczAoWbBbgIdVCmjRmEsyYMCe04DhFy8RQkBiWRSKC5udm1vLW1FX/+859xzz334KMf/SgA4Pbbb8fhhx+OpUuX4vjjjy9EdwgfuMntIihQUqaFyhB+J1pjly1QVC4ehQXFsrLl+FXb2KTSlqc7A+DfslUCRXTxZASLOoaFrSQbV1g23Fk8nt3khEcmSJYRKPHsrL/yUvf6AoUdrL531uFYsWEPHmWK+dnbhCVQ+OBH3jIjirqBTAqtE9tjWtjelnHhDa6r5DLl2O9W7uLJ/F+8YqoYFFUWT7+KuGc2iX1sdrJKL8rjMRiG4bL61EriMCrK4tjXnTnnAdXlWi4etwXF3XfesuX9fq1KM1b9tg3DQFVZHB29vwGdFxrWglKoEvGypiYOrccdlx4X2jFY2Puu1LE0BbGgrFmzBi0tLRg1ahRmzZqFDRs2AACWL1+OZDKJ6dOnO9uOHTsWw4cPx5IlS5TtdXd3o62tjftXatbv7MD3HngdG3d3+m8ccaLu4ulJmaH4QnUeOLaLR/VgV10f1WzFqm27fN5Y7WyY+1dsks4GC0hcPIrJ0EzBgsJaGlwWlCSfxeMHG2RrwOAe2JlZfHtnq5VcEz+RxsLGlmSq17r7xhagyxf2eJk0YcbF03vsP35+Mr531uE4cliDs86+rrs7ehwxMai2ghMQfJqxxIIC24Iii0GRl5eXx6CoXTyA90zHNqyrxv4siqpqySDNbjOgpsLVD5mLxxbLTh0USefZ8/QLN1IVavMSsZw1yLt5AKIFJYtR4OjOQuoG9r474NKMp0yZgjvuuANjxozBli1bcOONN+Kkk07CG2+8ga1bt6K8vBwNDQ3cPoMHD8bWrVvlDQKYN28ebrzxxrC7mhcX/b+l2NLahWXrd+OJOaeUujt5wT70ohgkq5Paq4POb0021TqL6q0/bVnOj8nPMpA0TV+Tel2vJWLO319VbiNaTNiJ+1icNGPOgpJ5gvpZUPxK13Fv9AafZhyPGY5rRmeyQC9YF08i5k7BBTIDXSEsKKLosY1GZxzhthLbx9/cG+jcWF2OikQ8UAyKlwVFds1iAYNkbQGkmsOGJR4znGPac9V4CRT7eGwmz4CactdAJ8viYY8pWw6IBfR8LCiKLB6vW4R1vwV28bB9K3SQbKmjV4tE6AJl5syZzueJEydiypQpOPjgg/H3v/8dVVX+E3nJmDt3LubMmeP83dbWhmHDhuXd13zY0jsT7drt7T5bRh92kIpimnFYMSg6Zlf77FWzw6rEEhuH4Dfw6llQ/H+apuW2fklL3feqFrbwmP3sFwVpp1BJ1k+vfvkvy53PoouHdT14TRbYrzzuHLcsbqCxuhzb2vgMJ9GCIutXuBYUQ/oZ8J5Q0r7HNvdavewKpOwAzcegyAbhYDEoqjoo/crjnoKtQiPAMvM7MJnP7v3Y79yJQWG2GVhd4brPuBgUoY+29VLWd3aJnwgQd7etfZ4WFKbfOs8LNounYDEoRdYiURoBCp5m3NDQgNGjR2Pt2rVobm5GT08P9u7dy22zbds2acyKTUVFBerq6rh/BzqdPSnfQSwsWDN9WNaKMEnlkGYsq9LKPpdUZc7twUfUJ637k719UVtQbPwCaZNpy9eCUitU65RhWbyAEAu3sdsBTJoxY0ERs5L2izEovr3IYghpxsm06QzusvoxPb2F2dj6E8Mb+0nFoViXRPb9ZWJQwnmksVVMxf54uUHtwXbz3swLjF15lP1euDRjSR0UrwBtW9SxFoqYId+nX3nC83eja0HJbi+3vFSXyywo2YE7k8Xj0a4g0uxVsiDZILVGRKuNfe28BEq/gC6emors75Q9XF82cETJiF5wgdLe3o5169ZhyJAhmDx5MsrKyrBo0SJn/erVq7FhwwZMnTq10F3pM/SkTEy44b846ocLi2LRiHoWj2rOGxW3PbceR/1oIW57bj23nH3DUQkx+8cpvp0d/aOF+Nerm5X7sQO9XzG3tGmiS1LWnSURl5fa59qxLEEYeWfxcC4eI7sPS6dYSTbA0ypm8G+g+3tMZ3CXpxlnlrHl8Yc39pOeA5deaihiUGJ6pe51SAiCiMXTguIIlF4LSm3GgsLew3yQrNuKoYoNMa1sO9ybvtLFE5cONvamOjEo7Lk7Lh5BVLGZW/bm7Dk2VrvTjL0sKF6TBQaZ78Y155BPmjEgunj876XqCrmg6dOzGRf5eF6ELlD+53/+B4sXL8Z7772HF154AZ/4xCcQj8dx4YUXor6+HpdddhnmzJmDp556CsuXL8ell16KqVOnUgYPw7a2LqTNzFt2ZxGsKOyA6vXwLRV+QaciP3z4Le7/Dswv3U9EiEGyadPC1+59RSksWKHgG4OS9nfxJNPyeVdYXJMFKlw8rjoo8Zjz8Be/77wsKOCDSfcn09k0Y8m52AMGa0EZ2r+fdFAVS8/LtJtqoM4F1qUkvnF71mtxuXgyFhT2fmMtEDKR4GXZsOcrYgWKWAjOpro87nkPqeYBYpEJNXE/zs0hWFBqKxKoLIu7s3i4IFmh/ohHkKwh2U6F6jJqB8lq3EpsUDgCWHeCIDvPYgXJlprQY1A2bdqECy+8ELt27cKgQYNw4oknYunSpRg0KDMV+S9/+UvEYjFccMEF6O7uxowZM/C73/0u7G70adgfUNDBORfYh1gUXTyZSrLhxqAkTRNV4N9e2R+myr+9+J3t0uVcFo9fHRQNgdKTMn3bEWcvztRBkW9rWdkAy/JEzHFhuSwoSTYGxT/NmEW8ZPt7UtlKsh4xKNXMoDCssUo6N1SZUDhN1a18Y1AmHFSfOR4bJBvXd/HYv9223rTiwfUZCwr7O2bvZZlI8LJs2K7BynI+VkJ22lXlCbR3J5Vtscc+Y9xg/Pctd8FM1ophCwnR6sMKTLsbtguqsabc6SML+/sSz9drgki2Hb9vWvXM8BKxrPDTuZPYIFn2x0IWlHAIXaAsWLDAc31lZSXmz5+P+fPnh33oAwb2BglSzCpXxLfwqJEMaS4etg2ZBYU9ddVb1p4O+QOftUT4WWeSaX8XT0/KlFYNFY/J6lfT9Ji3x7SYQm2MBUX4vtkZhXsCunhcAiWZ9g6Std0VrEDp308qskSLhqpf+WTxXPKREbji5FG9x/Ny8ajbEI9/UEMmMUDlppQJKi8Lii1s+WBOuaCurohLJ6S0t2SFQUM/ecwTex3KJVk8lWUxTkg4MSi9ImZAtVygsN4b8XztP6VpxsymfiJAtT7cINnsEMoGmIcagyJpq6AxLhEaAmiywAjCvm0FqRXBYpqW9pTfyQBv/6VAfLgHOTcW9sxkgayWxhtQl2TGXYAXeX7z9STT/mnG3SnTtx3T0ivUBgD7ulJOfElNRUIqHCzL4mYUTuXg4gEyA1lPysRHDhno6eJJOhaU7GNoWKN8bpUyl4tH3rN8YlBuOPcIph12Lp4ALh7hvrTPZ0xvWXkR2Vu+lwVFLlAMaTv9yhOeLzgVnEApl24Tl7hieIES5wKTs4XaMssG1FT0Lhfa9bCg2OdSJotBYS0ovjEo8uVeAqUyoIuHvRZ1VWzAbN+Nko3S7PYkUCIIm6aas0CxLMSQedO86t5X0NivHD86b7x0W3bwiKIFxbLcbqjyHAQKe24yKwe7RPUQ2y8pTw9kBy1VLRKW7pTp7+JJm1rpyqKLRzV4Llu/C6aVcaGwhcPYa5JM8y6iZNoM9DZlP5Of+eapeHXTXpx++GC8vTVTVNHLgsJe66H9q6TXT7SgqLRbaDEoHnVQvASKeHzbgjK2uQ5/vWwKhjRU+h7bS6DYh2atTspKsuVxz4wyLQuK5DqwLh5x/qNsobbMNgN7XTzuOijqGBRbvPi9hAQNktXZrypgHRT2vKrK4vjHlVO57yYMZN0obAxKARsPCAmUCMI+AHVcPJ09KW7uCyD7IFu3o92ZTffGc4+Q/ujZwSOKhdoAcAN6roG8aR83DDfNuEqgKISFWE7ei4yLRyMGxactSxAkpqmOQXlu7U4AwEdGDQQAqQWlW7AOtXWltGZvtrEHhOb6SjTXZ8oGqLJ4OrpTzr09cmA1KstiqCyLo76qTJlC7Hz2sqB4DGoxw9s9w7fDCiIhBsXj/mPH2qbaCi4r5MTDBmodWyf9V7SgqOqgJFPuvtqDKnuc+iq5QGHPvVySxSOmONvj9bghmVIQk4Y2cMud/TzSjL0sHKwg8KuNpBIYXvVTqgJm8bB09qRxzIjGQPtEkSgNASRQIgg7yPml2O5q78YpNz+Nk4SHn/0AZydzS/daVUSiXgcFABezkWsf+UBWiYuHMReoPAXditgRu086fevRsaCk3Fk8//zKVFRXJHDlX5bjvV2dMC1+wE2ZltIC9sK6XQCAqYcMAJB9SLODrWite+CVD3zPhUV2yezBhj2X+U+txc2Pr3aq5dZWJvDi96Y7c73IzoA198c8gmS9BrdEPOaIIsPwfhDLSrzb6Lp4VO4qP2QWlETM4O6tSi7NWD6Y1lYmvLN4mDa4bBThuDa2pYMVNjHD4LaxReqnjx2GU8c2YVBtBbfcxtvFo+wyT44WlDBL3bMUKgNS9t2GbaVhiZKLp+B1UAhvelImnnlnB1d/gnur9RnI1u/sQHt3Cis37uWW278V9mYTH6y7O3rwwtqd3EMsii4egLeg5FqrxS/Thn2+qB5iKsFomhb2dPRg8eodvv3o0QmSlbh4BtZUYGxzHZcZwz4UTdNSvt3baa+HNtUAAOL2JH4hxDvZyMYD20fPWgJvfnw1gGymS3kihrrKMmfQ1bGgqIJkvWJQymIGLjtxJI4c1oALjh7qeS4JIWtIF9biMKy/XuXsr512GPe3TKCIfWBdMnHD7eI54dABOHJYf88XHFZosJk4LLJgYbEWSVwiUAA44iSzXH0+KhePDJ04MdkxWHSzeHTdhd8+cyzGNtfiko+M0No+KGw3fvapSTi0qQb/e96EghwLIAsKwfDTx1bhT8+tx2ljm/DnS44FwMeE+FlQ7IFWfCO3Byr2ZhMH9jN+uRg723tw9PAG5TZRgXWt5CqiuEBWyXXlXDwBzbtpy8LH5z+PDRqTR+oEycosKLZJ25mrRXDxeKUZ24LIflO2B4EgYtgP2ZueLTq6UyY34zOLOEBJs3jYIFlDXgcF8P7eYjEDP/jYOADAjf9+U7ld5nj8ZIG6sPpoaH89C8qc00fjmIP74wu3vQhAbUGxi//XViQ4QZGp/5Ld9pszxmD2qYcC8HY5ssfpp3gjlxVqY4kZBidiVJdK/F7YP8VUa90AeL+tVOvDrIMCAF+Zdgi+Mu0QvY1zgO3GJycPxScne4vrfInSCEAWlBJz55L3AACLVmXra7Bvzn4xKPYA1SkEb8rMjaLVYGd7JgVxxYa9rvZKgVdKKzug5+riESfWcx2fdfEEDMJNm5aWOAGCuHj4PtoPTPthn3HxCFk8PtfGrttgDzzs/n5WnVxgS7Lf9OgqvLZpr2sbcUCWW1CEIFllDIreI80vzoMrra8RE2LDDq52kTYduNmOJcdj2x1QU+5yq7ACgL1+XoHW7HVXuQzY6ym7ZhkLCrNc8bMRB3vWSiK60HRfDnINiPYSQEHroBSDYmcERcmCQgIlgrDxEX5md3uwFrezev/0sqDIKGWQrKx/dhGvju6sCyznIFnT+8Gt4+JRESCWFD0ac/GkTMsVtCoKlLTFCxLVXDwsdmlunSDZMGBjJf7wzLs497fPu7YRBz5pFo9GJVl7nQ6ymXK548VztaCwQkJfoLBjkMqCwrYbFyxKvEDJ7nfioZnYtCrJxIAVOhYUj1gcu9+yGBQRVwwKGySrmM1YBntuwxpzm3zWKxO9MgcXz4FHdBQKuXgiSCqQBUURE9H7Sw4yiV2mvRIKFMnIVFdVho6eNCcocg6S5bJ43NfCDODf9mpbRUUihu6UiZ5UWhlsy9LZLQoUOwYg87cpzMWjms04u392oJKlGecbgyLDnmHY6ztzWVAU7djEY+oIV12B4jcPjVeasRfs8e0iZVr7eQSNZtrNLhtQLVhQYvxx2SvzhakHY0BNOY4d0YiP3PQk1ybn4imTDwVsuzJLUkyIf9ERKIbBWwVE15Hf5f73VSdiV0c3Dh5Q7bmdMotH08UTFRNKsbsRJQsKCZQSI7sZ2Ie5n0BRmXDtgYuvceLfn1IKFFn/aisT2NIqbpeji8fkB3MRdklQC8peScVOkeqKBLpTPVpz8QBwVQG1u2QPZpYQcyK6fFzHL0+45jlJFVigABlRtI+xgIm4LSjeQbIyC4o9GOmKCb+CblyacQAXT1ywdOgS87AoZPojWFCE6q3sabOXLxGP4eNHHsS1ZV8r9jg6WSGyfrmzeOT7ssvFIFixXS83nQULE4bW+/bVC/1S99FQKMU25ERIn5CLp9TIbgaukqxPkKxKUGTrcvQdC4qsf7WV7voMuacZZz8nZQIljyDZ7fu6fbexzeg9Kf8gWQD45j9e4/62H5i2yEibbrHmVWafnXlVZkHREU25UCFxL7CIb9CyM+DrkrhjUOxBz891Y+MXg+JV6t4Ltl92kTIdYr4WlOz6gUIMSjzGV5LVdYGyW+kIFJWLh6uDohjU2f6JMSDi+Xr1JdjbPX+NZJ9FxCkEPoxEabJAEighsPz9PTl/qbL92MHTL7NCNVjbzbKDvlYMSkgC5e0tbdjW1hVoH5UFRST3NGOmlopM+HExKMHa3qEhUOyS7jqF2mRkY1Ay/5fFnHhlfbETmxXTgsIGysoQszhkPyVxskDxFrAH+ONHNWLSsAbfPvm5eMokAuUvlx2HlvpK3PXF45T77e3MztVUJxHXKtjB0O7b3V+agpb6Stxx6bEu1xEr2MQgSt1fB/s7ksWoiMjEnwFePBqKy8qeX61Qc0UUi6p4GCB39wN7j3m7eNjrmtuxwqfIQbJFPZo3JFBC4IJbX8Cjb2wNrb0gacZ+MSjsAKRjeQgjSPb9XR2Y+atnMeX/FgXaT3ZsmQXFT6CoHix8PE64WTzbNcRYvwrWgpItGKaLva3dN0uYLBDwTitl57yRFmrrFU1hvzn6DX7ielmhKDFoVRT29hiZiMfw0OwTfNM+m2q93S9cmnGvWDnpsEF4Ye5pOHn0IOV+rFsuyHxRXNBo72B6wqED8cLc0zBtTJPLxcO5vMTD+PyG7fuIux80+qqMQZFMFijbzmaQcO1FsVjpcb/kWkSMFSieLh42fTs6CqWoRMiAQgIlLP61cnNO+8ldPPoxKCqTvj3+sut1YjfCKNT26qZW/40kyIRHLhYU1WOFHbv9ZjMuhIvHrkHSnTIdMVCtKJAlQ+riEZ4m2i4ep1Cb24JSragqmiteA45svW8dlFjM9RAVv6/Zpx6KC44eitsvPVZ6zDPGNePzxx+s7BNvQdF/TLIWlCBwLh6JEBAtKF4uC79fsH0fjT+oDpefNBI/+vgRPnvY/XL/JvRjUNQCRbTMeAna3C0oeq4b9thRGaiL7WqKyGkDoCDZSJIMkGasjEGxS68Lk+z5UcpS9+JgaxjyEtx+AiVmyOtkmJwFxXs248AWlAAxKMm06RyrqjyOdo8AUpa8XTwSC4rMxVNbkcC+Lr0+6eBnQXG5gPwEiuT7FQVKTUUCP//0JOUxYzEDPzpvPN7a0obl7+9xHy/HNONcBYpfmjFbOfag/lXYuCdbc8dd6E7++xhSX4ktrV346Nim3mMa+N7Z47T7yB5nYE0FdrZ349SxTVoxKOziplp+wsRywf0ii3WxyfXpxM4fpBuDopq5vNgUPUg2KsoMJFBKjjSLJ4gFxS9Illn/xTtewv+cMQYXeFQi3NXegy/e8RI+OXkozpowxPPYKnK9wUXhURaLSd++/dxQmcHKvY1vHRTms1e5bRnb9/m7eGyB0JM2YVqZ9nV8/zb2IOwEuEpmLxYtKGVxwzlX1jIiL9SWeSDXVCaA3IxgUip8YlDEayAbYLlCbXG3QAn7Ia4bVCkiZl7lcjyZBeV/PzEB/3l9Cw4ZVIODB1TjpfeyomrkQD7dVvXzeHD2CVj09nZ84qiD5BtIYM+c/Q7+ffUJeGrVDpx/9EFcPJXqe2AvYVOd2oJSVRYvSGEycf4gFawrSKcUAFFYyMUTEmFOsMRNFpirBcVx8WT339LahW/c96pne799ai2eXLUdX717hWZvvQkiVsRzScQNacCcb5Asl3IpTy1OS4KH7esejxmB4gcAYE+H/5uzHYOSTJnO9xpEoNg9ylaSdVtQxOwgVuDxWTzuWYYL5eLxO0cxy0f27XJBsobhGoSDWrxsLj1hBAC4JtvkJwvUf0x+44zRABB4Xha/LJ7Rg2txzfTROGdSCwDeqjOmuZbbVvXrGFxXiYumDA800RzbFuviGVJfhYumDEdlWRzVFQk0VpdjcF2FMjuKc/EI6desKPBzBwZ5zKqsUl73Cvu7j4wFJSLpzqWALCgRhB1I87WgeMUkFIO0afnWnLARB9tEzJAObv4unuznlGk5gw3bvm1V+MPidfjtk2vxty9PdR5c9VVlgR8J7GSPKlgLSrzXguKX4cJiP+TtN0zT9K9tU5GIYx9S3PGBbJYSX6gteFyMDn6Djvgdx2OGW6wKacbiHZBrQOPHJrbg8CF1GC7MPCweT5fLTxqFj45twsiBNYH64SdQXNszfRo9WBAoBfrJq4RaWTyGx6852VPY68agsFk0MoK8CLI9Ya+p7stHodLuo06EPDxkQQmLML/UIOXHpemyyA48ySA12HMgmTbx2qa9StHgNReIiCi2yuKxnAQK+8aRVMTg2Ndt3qOrsK87heseegOt+zNWkLrKROABTyd0p5+TZmw51gq/GiEcdhYPE4Niu7tGDZRX1WTftPtxacYSC0oOmUU6eImwmOGur3Hnpcehsboc8y862lnmTjP2jkEJwiGDalyDLz8Xj37bhmHg0KbawBYdLs1Yw2Kzqz0b8zRqkODiKVCYo5claVBtBRo9KueyX4+YQaVT0dYm1+csV4lY814pVNp9UIpfqC06CoUESkiE+ZUmA6QZqy0omf8XuvDa9x94A+f+9nn87qm1zjL2IRJEIIkZRIm4gcocXDzswz6ZkmcxidfNAtDWK1Dqq8oQIHFDG9bFYl+jQC4eJ0g2axGyXVgnHDpQug97LWo4F0/m/7JS97LA5HzwOsdKSczB1EMGYPn3p+PsidkYKL/JAsPOdOBnMy78Y1KWZuwFe/pshkpmZW59aKnPBK+yblU+BiX3i8yeX0sDP4cOe76y3ztLkFNjZ3wujwe3iBVi8sxcKLaDhywoByChWlBymM1YRFZJlluvIVz+9Oy72NnunZ3yt5c3AgB+tWiNs4xV4MkAbyFi8GsiprCg+Fxsdi0r8Nhr9cdn3sVGZvZh07LQ1tVrQakqg99jYWj/4BOV9ZO4ToK4eOwJIG0TtWllz0l0Udiw5ux+5X4WlIy17shhDbh4qjoFNyheLh6VeBFFi2uyQOG2yjUGRYVYWr/QBHXxfPrYYfjYxCGclckm18k077rsOMw4YjDuu3Kqs4yPQcl9uCiLx/C10w7DV6YdgmHCvcpaqyp9zj1ITNuY5lpcesIIfPvMsdwxdK1tfkUyD1SiJFAoBiWCBEkz9o9Bke+vk07840fexn/f2oa/f3mq77bsQzyV48R+riyeuCoGRf+asNYoVths39eNc3/7nPO3ZSHr4qnyrwB61oQh+OMz7/pux8JaUGx8gwIZ7EGTTTO2L3U/SduZbbPfC2sZsZ/XXKG23nutsiyOGz8+Hu/u7MCza3Zq90+F1znqnr9Yel4cqHSGnCAyg3UJeKW9hgUrJMXKujJqKhL4rUScALkPMIc21eIPnz9GuT5IsLCMOaePli6viGfvAR1xFoTrz8nUeHllQzbrSfc0ohKDUoisJi9yFbiFgCwooRHel8oO8NvbuvGVvy7Hore3SbdVDdbZUvfyfum6fl5cv1trO1agJD0sQP9YvglfX/CK1DIknkoiHpMGzfl4vbhzW/DSRlx97yvoSqZdVqM9TM2KjIsnE0yaKVHufX0+cdRB+N9PjMdAxYRwsrfu8t6ZfVl0BqOxzbX4f184xsmusUXHore345l3dgBQWyLinAWFLdSWOS5fqC3N9cnrjTnIM9NboOg9gvyCZMN+iIsWm0LDx6AEiEuSEObwEpaLx4uyhP61zvXcyjSzeFi6IhKDUmyiI09IoIRGuC6e7A9j9bZ9ePSNrbjszpfl2/oIEFnpc9O0Qg+e5QWKujjc/9z3Kh5auRn3r9jkasPt4jHc/nV4W1AsoTbIrxetwb9f3Yz7Xt4Iz3hdzsWT8M2OKYsbmDXlYBypmPdFJjwS8Rj3hlgej0kfltMPb+L+nnFEM04fN9j5237bfnLVdmdZPGZI3z7ZcbtGUgfF/n4siw3c7RUoHuJJR1hl+6Zep/tWXleVQF1lAk21FUhIYlC+dNJI7f7okMgxzThX4gFdPF4U6gU4HxePbrt+Aay5nht7DN3ziIqLp+gV9yOkUMjFE0FkM+2qSCtL3avTjJOmqdwvV1QCRTU3jO1OYXG7eGLSgdDLgqISbNv3dXvG3VhMn+qryrCnw7vglv1Gr3rWlSdi6OzhH3BlcYNbXp6ISf3hv73oaPxlyfv43/+87WzHItsnHjNQkYi5LFPsA5+bLJAJtF3w4gbc9NgqJ0jYFoXeAiWuHUTodTvrxgNUJOL4z9dPQiIWg2HwkwU+fPWJOKKlTqsdXXJNM84VI0SBUigTvTjrdFjkWhQvCKybboDC6ikSFQsKZfEQeSN+pZZlYc22fcoYEC/8xENnTwrv7+oA4J/FI7OU9KTM0EvasybxHolASaVNrN66z1kuexC56qDEDenbq12mvr07xQW6rt/ZoSzRblny8vbs+jYnzbjMN23YHkRUD1SZsCqLx1yxDbL9K8vimDyiv/O36BaSHTJuGFJXSowTKGwWT9aC8p37X8fezqRzzrbb5X/OGIN6RTxOEAuK13gZJEFmaP9+aO7NNGFjUMYfVB++i0cym3EhYe+DYhzPjy+dOBL9+5Xh8pNHOcsKZUlivzvRjSR+rTm7eJi+D6hRp0OzFDoDMqpEKASFBEouyN7ExcH1vpc34fRfPoOvL1gZuH0/98vpv3gGp9z8NN7a3Kb8EVkeFpSelBnKj48NImMHQja1145H+fY/X8eMW55xlkuFh1imPRaTvrXZ13razU/hpJ8+hXU72vH6plac+rOncf7vnpf2dU9nj6flxYLFWVD8fqR2PIfKAiB7Cy6Lx3hTs8KCAvAxJeIssjIzuGEY0ngOdlOu1H3vQCAzY9sxEMMa+2HFD07HCYcOcG8TQKB4vdHnWr9kSH3wLKqD+ssznWSw96dsFt+wYTVJvkIgjLlUvv+xcXj5+6ejuS47b05xhJqPtTDHc+MESrXfTNaZY6riy4rNh7mSLAmUHJCluYqujFsXrwMAPPL6lsDt+1V//WDvfgDA429u9Y1BkQmRnrSpdL0EwY7ZAPg3Gy4GpffzP4WYE7HfrfuTuGvJe9wy1cRhdvM72zNumMWrd+DelzYAAN7b1enaHgC2tXV7DpSWBbT1Wl/qqsp8zZx2uXCVBUXm507E+TgRL4HCWkPEWWRl1oJ4zOCOeeSwBtx2yTFCkGxWoIzqrXS6TBIEzVb6jMcMaap6WG/TuVo+fnPhUZg2ZhD+dsXxvtved+VUnDJ6EH43S571IiNRZIsG+zvN180R1gtwPGYETn/O+5jCqYuXItdzY/s+0MeC8sBXT8DJowfhri8el+PR+jYRMqBQDEouSAd9wV+ZyyPGNC3EYoanK0JEWUnWdvFI1vekTO0XEa+HcxsTR7KfibfQKTTXKczg+42/r8QTb2/nliXihnSgF4NkTcvC9jbvei3b93Whfz/1g4l18fhZUFgxoArqkwX3lgsWlLJ4TBnD4mVBkX0lnT0pTjTceelxqO9Xhl8uzNanqWayeMa11KGuMuGIMhaxxovsHgsyhh4ySF7lNmg7LCMGVuOOS/UGkGNHNOLOgIMNZ0EpgkBhB1BZOnoQwjTRGyFadnQQy9CLE3/mXklWPwZlwtD6SImTD/NsxmRBEXh/Vwe+du8reGtzm3IbHYGSi0JJOZk3ejeIpegL4O/i0Y1Bkb3Ndfak8I2/v4p/rvjAWdbRk3KO2cNZUOTH6RACSEVxArhjNmxkmmeHz2zCW1u7vC0oYGNQEp4/0n5M9VNlDIrE3eKyoMS9LCjZ7cQtZMfcvLeLiwsxej/aD/yKRMxVjXXKKLfrZkB1uWuyQNm9EuQtf8YRzfjBx8ZJ1+VTor5YFKOSbG1lGX570VH43ayjpQX9ghBmkCPrXiiGUBOP4Y5Bye3c2GeGbgxKVPgQJ/GQQBH58l+W41+vbsYnFLEMgPyBHca8DbbYkL2xVitKQOcSJNsdIAZF9lC67bn1+OeKTbj16XXOMsuCk52ik8WjM7leIiYPkhUtKGnTwvZ93haUne3dnlV5TdPCvm7WxaOGnQ02eJAsbzJXTVzGunhEl6LMLXLcyEbpfCN287Ly9aeNbXIts4NQWWSCWYwV8MIwDFx24kiMESa1Y/sZZeJFKNQGZCYuPGvCEP8NfSiUBaUoLh6pBSVLrue2j3FH14Y8lUOhKXahtggZUMjFI7KqN9PES3DIgmRFV4bOLSW+pSdNE1WIO6IjZrBCI1jBtbSXBSVA/IksQHCbwp0y+ccLsfibp0qDZEU6uv1rDIgDuk3atLhrZ1rwFSimBWxrU1tZOhjBVFOR8PyRsgXPVAKjXOLiKYtJ6qAoXUTZ7cTvmD3kbZccgwHVFZg0rIEXKL0b2e3LKs1+6phhGNJQhVTadOrsyDKBZII5l7dptiCXTR/QJ6HP81Nowkw+KbaLRxQoYQlY1pVZ7AE/X8iCQjjo3Lsyq4UrBkWjIXHgsdOLbVHxnZljufZdbgfLUsardCfT2Lx3v3R9kCwe2UCkepPqSpq498UNgSwoW1u7lCWlE3EDhmG4+pAyLVcqs8752EG1Muz0ZLueiJc7qKrcXU/E1XfJdStLGEKasdqCwt4/oshkH+KHDqrFpN5icWzGkzixYLXEbRCPGThl9CCcdni2CJxMEObr4rGRuUqi6uJpaajCwQP64bCmmkATOpaSWVOGwzCALzOpwfnCfj+FLPn/hakHozwRw5dPPoRb/puLjuL+zvXtvk1Sd6nPUHSFEh2JErpAmTdvHo499ljU1taiqakJ5513HlavXs1tM23aNBiGwf278sorw+5KTujcC7LBK5cgWfHBb7tjbFExrH8/vHr9GdljSAZi1cB8xV+W4yM3PYlXN7ZK+6obgyIrb+31JlVTkeBjUHrPRTSrdvSk8f6uDhw/bxE3Jw537N4BTTyeaVnc9d7Sut/zHOxZWtu71W4l2wRcXZ6JL9G1oKjqV8i+/0SMLzyXyeLx7DoA93fMCjrWn85m+9jCyR5fxLgSFbJJB2UiMxcLiizguQjhHTkRjxlYNOcUPHbNyX3mjft/PzEBq380EyMGqoOSg1IsC8oPPz4eb9wwwzWR4LQxTXjnxzOdv3MdOsOeoftAprE6OjE6od9xixcvxuzZs7F06VIsXLgQyWQSZ5xxBjo6OrjtLr/8cmzZssX599Of/jTsrhQMqQUlh7Rd8cGfLU+f+X9CqKTak+LTgy1FX1jslGSurwEsKHaQ3BsftOKJt7bhmXd24K0t6gDiRMzg+rivK4V/LN+E/YKVpLM7hf++mZlf6J1t7dK27Dc2USSlTF6geFlGAGBQnTuuQsS+HLWVmcJkXsF47Bs1NxlfZfYhKBvTyuMxfgr4hNrFwyLGoOzuyL4NsmKJtWzZ/bIFlJ9A+dVnj8TxoxrxzRljXetkbsJcLCgyF09ULShA5vdXjCqyYRJ2nAhvQSmsmlT1PYxz+u7Zh+P4UY344+cn591WsSn2HfjjT0yIzLUKXVY+9thj3N933HEHmpqasHz5cpx88snO8n79+qG5uTnswxcFaQxKDkGyYnyGPRDYVgcxzbYnZbqGzVwKriXTpnaFW7svH/uN3Moh0tGT5s7rxw+/LRVvHT1paaYLiy1MxLd107S4Y+z2KEt/yuhBgUp/2ymeXruwD0z2mV1dnsBeZwJC92MlETc4EVPu4eJhGSYUGNvbmT1fQzGA2O1mXTzeboqPH3kQPn7kQdJ1MjdhLhPHyQa4CQfVB26HKB6ViTgMI/P/KIi1XFNgD2qowoIr/GdlJ6J1rQpuYG1tzbgYGhsbueV33303Bg4ciPHjx2Pu3Lno7JQX2AKA7u5utLW1cf8KhY45V2a16OxJ46ePrXJ+QGwzqjgMUSTY7dpCpSyWGcBsS0J3yuSEkGlZvkXdZPSk9V083bLYFw/au1N6dVB6Ur7pm9n5bvjt0iYvCHcoAmS/Ou0Q/PrCo3pnJ9bDtjR4nTLri2cFBiu4ZLdRWTyG2krBgsLs//njD8YjXzvR+XvBFcfjW2eOwczxvJDf3SkXZDIBENO0oHghC3TOxfLBft8/+9QkfP20w3D1Rw/LuV9E4anvV4b/+8QE/PzTk0rdFQCRCo8oGn3FxVgICuqYM00T11xzDU444QSMHz/eWX7RRRfh4IMPRktLC1577TV8+9vfxurVq3H//fdL25k3bx5uvPHGQnbVQedWUFktfvf0Opw1YUhmbhCmpa5kWjp4iJk5tmCxB3h78CqPx5BMp9GTMgXhY+VkQQmSZpxMm65J77zo6E5pWZM6utO+qca2EBAvXdo00ZPO9mlnu1ugjBpUjW+dmXFXsKLAD9tX7eXiURXxYq1dsvuoLG5wsThlcb6I9bWnj+b8v8ePGoDjJbVKslYadb9svNKMdcllPikZ5YyL59gR/fHJyUNDaZcoLBceN7zUXXCI0kR2xeLDK08KbEGZPXs23njjDSxYsIBbfsUVV2DGjBmYMGECZs2ahbvuugsPPPAA1q1bJ21n7ty5aG1tdf5t3LixYH3WEateLgN74Ge3YWd97Uqm8fk/L8N3/vma68F/+i+fwdrt+xzrhj1AV/TGPPSkeQtKT8r0nbdHRpAg2Z6UyZW0l3Hv5cfjxEMHAgDau1KeZfS/deYYABkLimpiPxs7xVmM00hbFpcGLhNQrHCoEya8u2a6+q3dznbxujyq6eErmNgU2T1iGIYT4wK4S93rPohULi2xJD7bv34+Lh4vZCnuubzJspawfIuREcSHhQ+xAaVwAuWqq67Cww8/jKeeegpDh3q/KU2ZMgUAsHbtWun6iooK1NXVcf9Kwd7OHnSn0p5uFXsNO4CyWRe3Pr0Oz67ZiQUvbZTWWpl7/+tO+/YAbQ+IYpCsLKtHh56UfgyKaQF7OrwFyqhB1Tiz1w2RcfGo+/SxCS0AMqLCT/iU2bU8fIJkZbDuBDGD6IRDB6KpVl7uOuvi0bOgcC4ej9olNjWcBSXGPXx03Sa/7U29vPHcI7jlsmBC2zycj4tHdq8EieuxYQvs5VvOnfhw8qF08XyIbSihv8ZYloWrr74aDzzwAJ5++mmMHDnSd5+VK1cCAIYMyb+KYr5kbgb3r2B3Rw+O/tFCHDygH+ZfpJ50zJ4dtjuVFSW2QLEsi5sQb4/kTbhtfyobJGu7eHoHnu5UmhvQMkKjsDEogNyFwtK/X7kz8Hb0eFtQ+ldnLQh+8+eoLCimjkCJqy0oMcNQZgbUaATJstkonAWFFSiK/cUYFA7N59DHJrbg1DFNLtEhc/HYi/yCZL1orC53ZUrlEjDZwxTwq5QUsiMIPz6E+uRDTegCZfbs2bjnnnvw0EMPoba2Flu3bgUA1NfXo6qqCuvWrcM999yDs846CwMGDMBrr72Ga6+9FieffDImTpwYdneCwzx3Lcty3kCXvbsLAPD+rk7Pwd22irBuHTvFtitpYg8TP7BHEktgIRv4ag84FY5AMbm37CDpwixB91MFodqUJ2LOYNnenVYGxsaMjAXBMDICwKuyK5AVGWKmi1ioTbovs48Yg2IXY5PhWFA82taxoMgyvTJ9yYqliniMC4ALMubLLCIygfKJow7C1rZunCopa6/L7Zcch+8/+Dq+M/NwXPj/lgIARg6sRn1VGcriMWza04mPTWzxbYcVrjrZSwRBfLhdPKELlFtvvRVAphgby+23345LLrkE5eXleOKJJ3DLLbego6MDw4YNwwUXXIDvf//7YXclb7pTplP6u5J5AxVn4mXpcsQIa0HJPJjFQmF7JNkYppV9kNsDtP2m3ZMyOddFJpaksDEogL8FBci6Ljq61RaUikSmCFpNeQL7ulPSGi0sZbE8LCjMdRKzeOKGIS1FD2RrmXi5eLgYFE6gZNsc1sjPCGzDiqWyeIwzmuRbE0RmFTpz/BCcOT4/y+SEofV46KoTuWUGgN96WBJl5JKKTxAsR7SUxr1fSo5oqXemYPmwURAXjxfDhg3D4sWLwz5sQXjmnR14bVMrrpl+GDdIfm3BK8p97LRcWQxKh4ZAsSwrGyTbO8iWMxaUREweg2JbJXTo0aiDMmVkI5at3w0gmEBZu11edA3IpuEOH9APb25uw5ZWPQuK6E7QiUGJe1hQYjF18Sf7PDyDZNk6KIKL554vTcEL63Zh2phBuPdFdzC36OJhNUm+b0qySq1Rwsv1RxBePHbNSbh/xQf46rRD/Dc+wLjunHEYWFOurFN0IEOh9AKs6+OKvywHADT0K+NKgHtVLu1Oma7gV9vF47KgSGJQ1u3IVty1B7MKxoJSHuezeGwxU12e8CzlziKzoFQkYly/rztnHD77h6XY153yPN+xzZkZanWCHm2rxujBtXhzM1/LRjw+wMSgCALl1U17cewIvq6O61geMSjxmIEKxWBuZ/HounjqqrI/oUTcwEcOHYiPHDoQ2xXuKzZINh4zQrWg5FI8LSi2ED6hN2srCCRQiFwZ21yH75714bOeAEB9VRnmnnV4qbtREqL9ylVkVJPOvbWlDV2a5undHd04+9fPcsu6kmn8fvE6VzXW3R7ZMWdPGIL+vTUxbHdEjyB+2FiSIHUuZHVQxJiMsnjMmXxOZUH5xumjcc/lx2sf37aujR5cyy3/1pljsGTuaa7tnSweRqD0K4/jjQ/a8NgbW13bs4GgnjEohqGsYhs0i2fmhKz7hBWXTXWVuP+rH8GxI/pz+7IxKN0pk4tBydeCUozZZpd85zTccemxOH3cYP+NBXpyCOgmCOLDCwkUBtWsuu1dKeU6kTtfeJ8bqADg7S37cPPjq13b7lVUBAWAy07KZj/ZpvtuIc24m8nGCZK2KbOgVAlZHmXxmHNcVZDsOZNanMJiOmms9jHHNNdwyz82oUU6QZVtQWEtC+cdlTFzPr9up2v7ATXZ1GG25oYriydmKN0hNR6VZI/rtdqcMykrSuoqy3D9OeMAuAtaHT28Pw5t4sUY6x7KBD1n1+WbTnhGr2gYf1Dh3jSb6ysxbUxTTtUte1L6Bf8IgiDIxcPAZt6wrNnejhMPU5u0h9RXYuLQejz+5jZp4OcdL6yXWmZUJcsBYMSA7Iyk9tt+TyqNnpQQJNsrWGoClHOXzcUjTimfiBlOOq3KxcOKGp1CYHZmi2hBUVV6LZPMxTOkd+I/WZG3gTXl2LC7k9sXAGrKE1yMTiKmTjN25uKROHnuveJ4tHenUC8InktPGInzjx7qWg4AlR7zDXWn0pzZJN/Elqa6Srx2wxnoVxbNFF6aUZYgiCCQBYWhW/GG996uDmV5cSDj+x+kKPwFqIWPqs3yeAz9+/HpqACwq6MHjzKujbe3tDmpyjV5WlAqy9QWFJWLh91H543anpX3oIYqDKzJWkxqFALFjllhU1IbPKYCH8hZUPj5ctjBMWao04wPbcpYd2TJUfGYIRUhAJTLrzr1UBzRUudYWVi6hfsijJl96yrLHMtT1Lj5U5MwZnAtbp0VLPuHIIgPJ/RKw6ASEpYFvCUEdbKMG1LHpZjqoipZPrCmnBvw7bf93zwpr7QLBI1BSbssOqJAScTVqbg2otXFj2zGkYGvTx+NHzz4BgB17IQtMthMGVa4ibAuHrHNusoyx+oSV1hQzpnU4sSIhDXnx4CaCjzytZOk6w5qqOJdPAd4vYPRg2vx+LUn+29IEAQBsqBweMWZvLerQ7r888cfjP/7xARPU75hAGdPdNeiaN0vt6CIFgXV2z63T4W+i6d1f9IdgyJaUGIx6dwu3DbC+t/NOhpHDmtQbs+KolnHDcc3Z4zBrz57pG/7rDWkfz+5BWV4Yz/UMdetqY63aLFuJFGgjBpUjStPOQQ/vSBbKLCQJbXv/tIUXHrCCHx+6sFc3MmHedZSgiAIERIoDDIXz6Sh9QDkAmVYYxV+dN54DKip8LSgfHxSixNgCWTSlr0QJ1JTxUuwBHHx7O1MumNQymUWFO/jigPqWROG4E8XH6PcPs2M+rGYgdmnHuqZ22+7bFiBorp2Rw9v4ITcsP79uPVssbaMiyd7vqeMHoTvzBzLXYPph/NZKh8/0r9Sqi4nHDoQ159zBCrL4ge81YQgCCJXyMXDIMYE9O9XhnEt9Xh1U6vU/bOfmUVXZeX47UVHYfrhg/H4m9nYkea6Ss+YFjEjR0ugKOI4ZOzp7JG4ePhjJOJGTmmrA2sqcN+VU/HE29vwh8Xvcut0rRKJmIH5s47GxF5xyAsUuQVl8sH90cYEzg7tz1dyZeuViBYUWUbPpSeMQEtDFcYOydRsOS2PUvFekD4hCIKQQxYUhi7BgjJ6cC2ae7NGZHT6CJRzJ7XgYxNbUFkW51wTzfXqNgGgqkx08fhbR8YNqffdxqZ1f9I1l40Y5FkWi3kKo9GDa5Trjh3RiGMP9i6k5sXgukrMOKLZsdDEfGJQYgZw9sQWTmgMa+QtKGz9kbhh4KAGRsBIVEIiHsPZE4fgkEE1OHdSS16zAXtBFhSCIAg5JFAYRCvJmOZaDBZiGb522mHOZ1agsEGmx41sxIvfPQ2//MyRzjJWoAxhBIos+2NQLW8l0LGgnDJmEJZ9113sTLa/abkziIY3VnN/e9UK+d5Zh+PfV58oXWdTptFnFeL8QmyacVVZnDufq049FCt+cDoaq8u5SrqcAAG4+JR43MD5R2ddS7s9KuUWGoo7IQiCkEMChYENkh3avwrnTGrBYMHaMay/fBI4tjJpfVUZmuoqlbETxxzciHFD6lBVFselJ4zg2hk1sBrXTB/NLRPdFTISMQOD6yrxh89PxrDGKvzzK1OddeJkeQCfOjy2uRafn3qwaxtWZLBZQi0NVb5WHVEgBCElVBxlr6NhGJwVpaFfmeP22cGck5iVJFpQ+pUn8LtZR2PkwGrpuRcLkicEQRByKAaFwS4jP/3wwU6wp5heLA58NuyALRMErECpqUzgP1/Ppp7e+cJ72NOZxOjBNfjvtae49j1+1ADfvtuD+IwjmjHjiGZuXV1lwhEkA6rLsaujx6kO+80ZYzD71EMz/arg5/Op5Gbn7Ye3t7T1nqu/rj1kULVr2aiB7mUy7FokNjGhgllDVTm2tXW7+jLAo0YKG4NiF5k9a8IQnDUhv5l+CYIgiMJAAoVhcF0FTh49iCsVLrp4VLU/2IFS5rZhLRDipGl/+/JU/PGZd/F1xn3EwhYgUxH3cBXUMv0ZVFuBXR09jmBhrROim+TUsYPwzxWbAAANTBuqeWxYjN5iaLbo++TkobiqVwipeORrJ+KO59/DtafzFiQx25kVe6y75/KTR2F7WzfOlWTciBaUqBBGcTaCIIgDEXLxMHx07GDc9cXjOBdLY3U5F4uhsqCwy9m3dRs21mCkYEkYPbgWP/vUJFdgJ9837ywS0crAchRTm2RAbwVXu3x9QhAoLGeNz1oXpozKBr3qFqX7Su/U6IcPqcPPPjUJI3wsKEe01OPmT01Ci+AeYufVATJBtDasQKmrLMNPPjlROtMua9USZ0cuJaRPCIIg5JAFxQfDMHBQ/yqs35mpg1JVHkNVWRz7haJufhYUAHj46hOxac9+HNGin3FjM/+io/HI61vwP/e9qr3PI187ERt3d6I7ZeKOFzLLxDRddrAWXSSxmIFl3z0Nz63ZiUnD6nHLE2sA6Ll4AODqjx6GlvoqfORQfxeVF2Ks7meOHYZ/vboZgDteRYVdqM0wohWYGqGuEARBRAqyoGjABqlWJOL47lljAWSqyLLLbWQxKAAw/qB6nDm+WbrOj6ryOD45eah0nWqiviNa6nHmeD7GQkzTZa0RshmFB9dV4oLJQ1HFFI9TWZFE4jEDnz52GIb2V1uGdNth+cghAzD54P4AgEkelWtZ7BmNo+TeAfKfwZggCOJAhSwoGrADbGVZHJ87/mCcdNggDGdcMmIWT6E5Y9xg/Oi88TAMf8HAFkhj052HN/bD6eOyFVMHe9R8Ya0mOmnPYSKmOxuGgXsvPx4727td7iAVtgXFyxVWCiKmlwiCICIDWVA0GNaYHQSryuMwDAMjBlZzgx2b8VJXQIHyvbMOR0t9Jb539uEYXFeJptpKpcXGhp347pTRg1BbmUBFIobvn304Vy32SyeNxMED+uHLJ49ytcEKlGKPqbM/eigOaqjCtUxsUHkipi1OgIwYmzi03pXhVGqi5G4iCIKIEmRB0YCt6VGpsB4Uy4Jy+cmjcLlEQHjB9ueYEY14/YYZ0u0a+pVj8TdPla5jXVg6WTxh0lRbiee/89G82iiLx/Cvq7yLy5WChiJY2wiCIPoiJFA0aKrNuj7ESfVsWDeEKiakVEwb3YRZU4Zj/EHBg3NtyhMxfHPGGHT2pDCkPvcibATPSYcNxOeOHx5oqgKCIIgPAyRQNGCLjqlSbFnh4jdbcbGJxQz87ycm5N3ObJ86JkRwDMPAj8/L/7shCII40CCBokFTXSX+fPExKIvHlDU0KsviuOPSY2GBLwpGEARBEERwSKBoctrhg323mTbGu5gaQRAEQRB6UBYPQRAEQRCRgwQKQRAEQRCRgwQKQRAEQRCRgwQKQRAEQRCRgwQKQRAEQRCRgwQKQRAEQRCRgwQKQRAEQRCRgwQKQRAEQRCRgwQKQRAEQRCRo6QCZf78+RgxYgQqKysxZcoUvPjii6XsDkEQBEEQEaFkAuVvf/sb5syZg+uvvx4rVqzApEmTMGPGDGzfvr1UXSIIgiAIIiKUTKD84he/wOWXX45LL70U48aNw+9//3v069cPt912W6m6RBAEQRBERCiJQOnp6cHy5csxffr0bEdiMUyfPh1Llixxbd/d3Y22tjbuH0EQBEEQBy4lmc14586dSKfTGDyYnyF48ODBWLVqlWv7efPm4cYbb3QtJ6FCEARBEH0He9y2LMt325IIlKDMnTsXc+bMcf7+4IMPMG7cOAwbNqyEvSIIgiAIIhf27duH+vp6z21KIlAGDhyIeDyObdu2ccu3bduG5uZm1/YVFRWoqKhw/q6pqcHGjRtRW1sLwzBC7VtbWxuGDRuGjRs3oq6uLtS2iSx0nYsDXefiQde6ONB1Lg6Fus6WZWHfvn1oaWnx3bYkAqW8vByTJ0/GokWLcN555wEATNPEokWLcNVVV/nuH4vFMHTo0IL2sa6ujm7+IkDXuTjQdS4edK2LA13n4lCI6+xnObEpmYtnzpw5uPjii3HMMcfguOOOwy233IKOjg5ceumlpeoSQRAEQRARoWQC5TOf+Qx27NiB6667Dlu3bsWRRx6Jxx57zBU4SxAEQRDEh4+SBsleddVVWi6dYlJRUYHrr7+ei3khwoeuc3Gg61w86FoXB7rOxSEK19mwdHJ9CIIgCIIgighNFkgQBEEQROQggUIQBEEQROQggUIQBEEQROQggUIQBEEQROQggcIwf/58jBgxApWVlZgyZQpefPHFUnepT/HMM8/gnHPOQUtLCwzDwIMPPsittywL1113HYYMGYKqqipMnz4da9as4bbZvXs3Zs2ahbq6OjQ0NOCyyy5De3t7Ec8i+sybNw/HHnssamtr0dTUhPPOOw+rV6/mtunq6sLs2bMxYMAA1NTU4IILLnBVbt6wYQPOPvts9OvXD01NTfjmN7+JVCpVzFOJPLfeeismTpzoFKuaOnUqHn30UWc9XefCcNNNN8EwDFxzzTXOMrrW+XPDDTfAMAzu39ixY531kbvGFmFZlmUtWLDAKi8vt2677TbrzTfftC6//HKroaHB2rZtW6m71mf4z3/+Y33ve9+z7r//fguA9cADD3Drb7rpJqu+vt568MEHrVdffdU699xzrZEjR1r79+93tjnzzDOtSZMmWUuXLrWeffZZ69BDD7UuvPDCIp9JtJkxY4Z1++23W2+88Ya1cuVK66yzzrKGDx9utbe3O9tceeWV1rBhw6xFixZZL7/8snX88cdbH/nIR5z1qVTKGj9+vDV9+nTrlVdesf7zn/9YAwcOtObOnVuKU4os//rXv6xHHnnEeuedd6zVq1db3/3ud62ysjLrjTfesCyLrnMhePHFF60RI0ZYEydOtL7+9a87y+la58/1119vHXHEEdaWLVucfzt27HDWR+0ak0Dp5bjjjrNmz57t/J1Op62WlhZr3rx5JexV30UUKKZpWs3NzdbNN9/sLNu7d69VUVFh3XvvvZZlWdZbb71lAbBeeuklZ5tHH33UMgzD+uCDD4rW977G9u3bLQDW4sWLLcvKXNeysjLrvvvuc7Z5++23LQDWkiVLLMvKiMlYLGZt3brV2ebWW2+16urqrO7u7uKeQB+jf//+1p/+9Ce6zgVg37591mGHHWYtXLjQOuWUUxyBQtc6HK6//npr0qRJ0nVRvMbk4gHQ09OD5cuXY/r06c6yWCyG6dOnY8mSJSXs2YHD+vXrsXXrVu4a19fXY8qUKc41XrJkCRoaGnDMMcc420yfPh2xWAzLli0rep/7Cq2trQCAxsZGAMDy5cuRTCa5az127FgMHz6cu9YTJkzgKjfPmDEDbW1tePPNN4vY+75DOp3GggUL0NHRgalTp9J1LgCzZ8/G2WefzV1TgO7pMFmzZg1aWlowatQozJo1Cxs2bAAQzWtc0kqyUWHnzp1Ip9OuMvuDBw/GqlWrStSrA4utW7cCgPQa2+u2bt2KpqYmbn0ikUBjY6OzDcFjmiauueYanHDCCRg/fjyAzHUsLy9HQ0MDt614rWXfhb2OyPL6669j6tSp6OrqQk1NDR544AGMGzcOK1eupOscIgsWLMCKFSvw0ksvudbRPR0OU6ZMwR133IExY8Zgy5YtuPHGG3HSSSfhjTfeiOQ1JoFCEH2Y2bNn44033sBzzz1X6q4csIwZMwYrV65Ea2sr/vGPf+Diiy/G4sWLS92tA4qNGzfi61//OhYuXIjKyspSd+eAZebMmc7niRMnYsqUKTj44IPx97//HVVVVSXsmRxy8QAYOHAg4vG4K1p527ZtaG5uLlGvDizs6+h1jZubm7F9+3ZufSqVwu7du+l7kHDVVVfh4YcfxlNPPYWhQ4c6y5ubm9HT04O9e/dy24vXWvZd2OuILOXl5Tj00EMxefJkzJs3D5MmTcKvfvUrus4hsnz5cmzfvh1HH300EokEEokEFi9ejF//+tdIJBIYPHgwXesC0NDQgNGjR2Pt2rWRvJ9JoCDzAJo8eTIWLVrkLDNNE4sWLcLUqVNL2LMDh5EjR6K5uZm7xm1tbVi2bJlzjadOnYq9e/di+fLlzjZPPvkkTNPElClTit7nqGJZFq666io88MADePLJJzFy5Ehu/eTJk1FWVsZd69WrV2PDhg3ctX799dc5Qbhw4ULU1dVh3LhxxTmRPoppmuju7qbrHCKnnXYaXn/9daxcudL5d8wxx2DWrFnOZ7rW4dPe3o5169ZhyJAh0byfQw+77aMsWLDAqqiosO644w7rrbfesq644gqroaGBi1YmvNm3b5/1yiuvWK+88ooFwPrFL35hvfLKK9b7779vWVYmzbihocF66KGHrNdee836+Mc/Lk0zPuqoo6xly5ZZzz33nHXYYYdRmrHAV77yFau+vt56+umnuXTBzs5OZ5srr7zSGj58uPXkk09aL7/8sjV16lRr6tSpzno7XfCMM86wVq5caT322GPWoEGDKCVT4Dvf+Y61ePFia/369dZrr71mfec737EMw7D++9//WpZF17mQsFk8lkXXOgy+8Y1vWE8//bS1fv166/nnn7emT59uDRw40Nq+fbtlWdG7xiRQGH7zm99Yw4cPt8rLy63jjjvOWrp0aam71Kd46qmnLACufxdffLFlWZlU4x/84AfW4MGDrYqKCuu0006zVq9ezbWxa9cu68ILL7Rqamqsuro669JLL7X27dtXgrOJLrJrDMC6/fbbnW32799vffWrX7X69+9v9evXz/rEJz5hbdmyhWvnvffes2bOnGlVVVVZAwcOtL7xjW9YyWSyyGcTbb74xS9aBx98sFVeXm4NGjTIOu200xxxYll0nQuJKFDoWufPZz7zGWvIkCFWeXm5ddBBB1mf+cxnrLVr1zrro3aNDcuyrPDtMgRBEARBELlDMSgEQRAEQUQOEigEQRAEQUQOEigEQRAEQUQOEigEQRAEQUQOEigEQRAEQUQOEigEQRAEQUQOEigEQRAEQUQOEigEQRAEQUQOEigEQRAEQUQOEigEQRAEQUQOEigEQRAEQUQOEigEQRAEQUSO/w+PBRYLPkQi8gAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "# plt.plot(losses)\n",
    "plt.plot(score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "procthor-rl",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.20"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
